Laminar stabilization control in chaos intermittency

Institute of Electronics, Information and Communication Engineers

Intermittency gives the irregular switching of almost periodic (laminar) and chaotic (burst) motions, and can be confirmed in every physical system within a certain parameter range. In this paper, by utilizing a method stabilizing a periodic motion which have been vanished by a saddle node bifurcation, a laminar motion in the intermittency is stabilized by a tiny control input. Apseudo periodic orbit composed by the vanished orbit or laminar motion is applied as a reference for the linear feedback control. We demonstrate the control for a circuit model and show robust stabilization responses.


Numerical Analysis of the Estimate of Noise Covariance by using Best Linear Unbiased Estimate

2022 IEEE Conference on Control Technology and Applications

This paper derives a BLUE (best linear unbiased estimate) for the estimation of noise covariance matrix in the framework of subspace identification. An estimation method of the noise covariance with a CCA (canonical correlation analysis) weighting matrix was previously proposed by the authors. The newly derived BLUE is compared with the previously proposed method numerically and it is shown that the previously proposed method gives a good performance.


An Introduction of a CCA Weighting Matrix to a Closed-Loop Subspace Identification Method

Preprints of the 19th IFAC Symposium on System Identification

This paper introduces a CCA (canonical correlation analysis) weighting matrix to an estimation method of the innovations model previously proposed by the authors. A numerical simulation illustrates that the CCA weighting reduces the covariance of the estimate and that the proposed method gives similar or better performance compared to Closed-Loop MOESP and PBSID. Especially, the design parameter called "past horizon" in the proposed method can be set small compared to Closed-Loop MOESP and PBSID. It is also analyzed how the bias of the estimate is reduced.


On consistency of output-error closed-loop subspace model identification for systems compensated by general LTI controllers

This paper deals with consistency analysis of a closed-loop subspace model identification method for systems compensated by general linear time-invariant feedback controllers. The results presented here are a straightforward extension from the authors' recent work for systems compensated with constant gain feedback controllers. It is shown that the estimate of the extended observability matrix, which is obtained from our closed-loop subspace model identification method, is consistent up to a similarity transformation


Minimal realization of an unstable plant under feedback

This paper formulates a problem of finding a minimal realization of an unstable plant under feedback, given an infinite string of input-output data of the plant. It is assumed that the plant and noise models have non-common state matrices and that the plant is stabilized by a strictly proper linear-time-invariant controller of the finite order. A minimal realization of the innovation form has been estimated using closed-loop subspace identification algorithms, whereas that of the plant is determined in this paper. A matrix is proposed to avoid a numerical problem of a traditional algorithm applied to an unstable plant.


On error analysis of a closed-loop subspace model identification method

24th International Symposium on Mathematical Theory of Networks and Systems

This paper studies error analysis and asymptotic variance of a closed-loop subspace model identification method for a system described with the output-error state-space representation. For details, since the procedure of the identification method includes the QR factorization of stacked data Hankel matrices, this study investigates asymptotic properties of block elements of the triangular matrix obtained from the QR factorization. The set of the block elements is separated into two components, namely, the signal-based component and the noise-based component. The contributions are to derive asymptotic properties of both components and to obtain the asymptotic covariance matrix of the vectorization of the noise-based component.


Consistency analysis of the extended observability matrix of output-error closed-loop subspace model identification

Preprints of the 21st IFAC World Congress

This paper studies statistical properties of a closed-loop subspace model identification method for a system described with the output-error state-space representation. For details, the limit value of the matrix to be singular-value-decomposed to estimate the extended observability matrix is investigated. The contribution is to ensure that the estimate of the extended observability matrix is consistent up to a similarity transform.


Subspace identification algorithm for stochastic systems equipped with zeros close to unit circle

In identifying a stochastic system possessing zeros close to the unit circle, the effect of the initial state appears in the estimates. This paper derives a stochastic subspace identification algorithm for such a system. A new stochastic realization algorithm is developed based on the covariance matrices of the state and the white-noise input, by taking the initial state and positive realness into account. A subspace identification algorithm is obtained by applying the realization algorithm to a finite string of data. Numerical simulation results show that the proposed algorithm provides favorable results compared with the conventional ones.


An Estimation Method of Innovations Model in Closed-Loop Environment with Lower Horizons

This paper proposes an estimation method of the innovations model in closed loop environment by using the estimate of the innovations process. The estimate of the innovations process from the finite interval of data has a bias, so are the estimate of the proposed method. However, it is analyzed that the bias can be reduced. The Kalman gain and the covariance of the innovations process are estimated by using a semi-definite programming problem previously proposed by the authors. Numerical simulation illustrates the proposed method gives better performance than Closed-Loop MOESP and PBSID when the data length is large and the past horizon is selected low.


Modeling of parameter dependent systems using the impulse response and the kernel method

Proceedings of SICE Annual Conference 2019

The Society of Instrument and Control Engineers

This paper analyzes the covariance of the estimated system matrices in PO-MOESP method and applies the result to the calculation of the covariance of the coefficient of the transfer function obtained from the estimated system matrices. In the analysis of the covariance of the system matrices, consistent estimates of Kalman gain and the covariance of the innovations process are utilized. Unscented transformation is applied to calculate the covariance of the coefficient of the transfer function. A numerical simulation is shown to illustrate the validity of the analysis.


Covariance of the Coefficient of the Transfer Function Estimated via PO-MOESP Method


On the Uniqueness of the Estimate of Innovations Model

In this paper, a sufficient condition for identifying the Kalman gain and the covariance matrix of the innovations process is derived. Under the condition, the innovations model can uniquely be estimated in a generic sense by solving a semidefinite programming problem proposed in the previous work of one of the authors. A numerical simulation illustrates the consistency of the proposed estimate.


On the Uniqueness of Spectral Density Function in an SDP Problem for the Estimation of Innovations Model

Proceedings of SICE Annual Conference 2018

The authors proposed an SDP (Semi-definite programming) formulation for the identification of the Kalman gain and the covariance matrix of the innovations process, aiming at a consistent estimation. However, the rank of a coefficient matrix of the variables does not have a column full rank, so that the uniqueness of the solution is not clear. In this paper, it shown that the Kalman gain and the covariance of the innovation can be uniquely estimated under a certain condition.


Closed-loop subspace identification algorithm based on finite-interval realization

International Federation of Automatic Control

A closed-loop subspace identification method is developed based on the realization of finite-interval data. By making a new assumption, it is shown that the proposed method shorten the period required for the one developed earlier by the authors. Numerical simulation shows effectiveness of the proposed method.


Closed-loop subspace identification taking initial state into account

18th IFAC Sympotium on System Identification (SYSID 2018)

This paper develops a closed-loop subspace identification algorithm for the systems with the stochastic part possessing zeros close to the unit circle. The proposed algorithm estimates the system under the feedback loop, taking the effect of the initial state into account. A nonlinear optimization technique is applied for computing the Kalman gain and the covariance of the innovation. Numerical simulation shows that the proposed algorithm successfully provides models by taking the initial effect.


An SDP Formulation for Consistent Estimate of Innovations Model

This paper proposes an SDP (Semi Definite Programming) problem formulation aiming at consistently estimating Kalman gain and the covariance of the innovations process. In the previous papers, the problem of estimating these parameters was formulated as a nonlinear optimization problem. It is shown that the problem can be reformulated as an SDP by extending the variables. A numerical example illustrates the consistency of the estimates.


An LMI Problem for Consistent Estimate of Innovations Model

Proceedings of SICE Annual Conference 2017

This paper proposes an LMI problem for consistently estimating Kalman gain and the covariance of the innovations process. In the previous papers, the problem of estimating Kalman gain and covariance consistently is formulated as a nonlinear optimization problem. It is shown that the problem can be reformulated as an LMI problem by extending the variables and that the solution gives consistent estimates. It is also shown a sufficient condition for the existence of a consistent estimate.


Identification of Linear Stochastic Systems Taking Initial State into Account

This paper derives an algorithm for identifying linear stochastic systems, by taking the effect of the initial state into account. The proposed algorithm computes coefficients by using the extended observability matrix for deleting the effect. Numerical simulation shows that the proposed method outperforms existence methods.


Investigation of Effects of Silica Particles on Light Extraction Efficiency of Light-Emitting Diode Packaging Using Ray-Tracing Simulation

This paper reports an investigation of the effects of silica particles contained in mold resin to the light extraction efficiency of light-emitting diode (LED) packaging (PKG) using ray-tracing simulations. The light extraction efficiencies, namely, luminous and radiant flux of the rays emitted from the PKG, are improved with the silica particles. The silica particles cause the scattering of the rays in the mold resin of the LED PKG. If the scattering can be generated properly, the light extraction efficiency can be improved. Varying the volume percentage of the silica particles, its effects to the light extraction efficiency are confirmed. Finally, we could confirm the improvement of luminous higher than 0.5 lm and radiant flux higher than 0.02 W respectively from the simulation results.


Optimization of a Light Reflector Angle of Surface-mount-devide Light-emitting Diode Packaging Using Ray-tracing Simulation

Proceedings of SICE Annual Conference 2016

This paper presents an investigation of the optimization of the light reflector angle of top-view (TV) packaging (PKG) of a surface-mount-device (SMD) light-emitting diode (LED) using ray-tracing simulations. The light extraction efficiency which is the total relative intensity of the rays emitted from the PKG, is used for the evaluation index of the output performance of the PKG. The cavity angles composing the light reflectors of TV PKG with round and square-shaped cavity are optimized on the basis of the optical design method proposed in our previous study. In addition, the optimal angles under the conditions where the mold resin contains a phosphor affecting the light extraction efficiency intensively, are investigated. Finally, we suggest important factors for designing the light reflector (cavity) angle of TV LED PKG.


An equation for estimating Kalman gain based on the finite-interval stochastic realization

This paper derives an equation for estimating the Kalman gain based on the finite-interval stochastic realization. It also proposes an algorithm for better estimates for the Kalman gain, by improving the finite-interval realization algorithm. Numerical simulation shows that the proposed algorithm successfully improves estimates of the Kalman gain.


Consistent Estimates of Kalman Gain and the Covariance of the Innovations Process

A problem of estimating Kalman gain and the covariance of the innovations process consistently was proposed by the author, which is formulated as a BMI problem and an algorithm solving the problem is also proposed. However, a given numerical solution requires computational costs. In this paper, in order to reduce the computational costs, an algorithm by using a Cholesky factor of some positive definite matrix is proposed.


Consistent Estimate of Innovations Model

Preprints, 12th IFAC International Workshop on Adaptation and Learning in Control and Signal Processing

International Federation of Automatic Control

This paper proposes a numerical solution of the consistent estimates of Kalman gain and the covariance of the innovations process in the innovations model. The estimates are given as solutions of some BMI problem, so an iterative algorithm for solving the problem will be required. An algorithm for a class of BMI problems in which the objective function is nonincreasing is applied for solving this problem. Explicit formulations of the estimation error in PO-MOESP method are also derived and shown to be consistent so that the consistent estimate of all the parameters in the innovations model are obtained.


Frequency Domain Uncertainty in PO-MOESP Method

Proceedings of the 47th ISCIE International Symposium on Stochastic Systems Theory and Its Applications

This paper provides an estimation method of the uncertainty of the pulse transfer function from the covariance matrices of the estimates of the vectorized system matrices in PO-MOESP method, in which Kalman gain and the covariance of the innovations process are consistently estimated by using the previously proposed method by the author. In the conventional methods, the estimates of Kalman gain and the covariance of the innovations process are asymptotically consistent, i.e., the estimates converges in probability to the true value as not only the data length but also the past horizon go to infinity. The contribution of the paper is to provide a consistent estimate of the frequency domain uncertainty for fixed past/future horizons.


Consistent Estimate of Kalman Gain in Subspace Identification Method

The 2015 IEEE Control Systems Society; Multiconference on Systems and Control

This paper proposes a consistent estimates of the Kalman gain and the covariance of the innovations in the framework of subspace identification. The estimates of the system matrices in PO-MOESP method are known to be consistent while the estimates of (A,C) matrices in N4SID method are asymptotically consistent, i.e., each estimate converges in probability to the true value if not only the data length but also the past horizon goes to infinity. The conventional estimates of the Kalman gain and the innovations covariance are asymptotically consistent. To consider the gap relating amount, consistent estimates of the Kalman gain and the innovations covariance are obtained.


Estimation Error Analysis of System Matrices in Some Subspace Identification Methods

Proceedings of 10th Asian Control Conference

Explicit formulation of dominant parts of the estimation errors of system matrices in PO-MOESP method and a robust N4SID like method are derived based on a proposing lemma on a gap between two singular subspaces. By considering the gap instead of the perturbations of the singular vectors, it is shown that the estimate in PO-MOESP method is unbiased while the estimate in the robust N4SID like method has an asymptotic bias when the past horizon p is finite.


Effect of Optical Properties of Components for Light Extraction Efficiency of SMD LEDs

This paper reports effect of optical properties of components for light extraction efficiency of surface mount device (SMD) Light emitting diodes (LEDs). Improvement of light extraction efficiency of SMD LEDs is very important because SMD LEDs are used for various electronic instruments. In particular, side view (SV) LEDs are used for a back light of a liquid crystal display (LCD) widely. A cavity of SV LED packaging (PKG) is narrower and frequency of reflection of light is higher than those of general square SMD LED PKG. Therefore, light extraction efficiency of a SV LED is affected by optical properties of components intensively. In the paper, effect of reflection methods of a PKG resin and silver-plating of a lead frame (L/F) is investigated with using ray tracing simulation. In addition, simulation results of SV LEDs and of square SMD LEDs are compared.


Error Analysis of State Approaches in Subspace Identification Methods

Explicit formulation of dominant parts of the estimation errors of A and C matrices in shift invariance approach and state approach are derived based on a proposing lemma on perturbations to the singular subspaces. In the state approach, two methods are considered: one estimates the state from the left singular subspaces of a certain matrix, while the other estimates the state from the right singular subspaces of the same matrix. It is shown that the estimate in the state approach has an asymptotic bias when the past horizon p is finite, while the estimate in the shift invariance approach is unbiased.


On the precision of the plant estimates in some subspace identification methods

Preprints of the 19th World Congress IFAC

Explicit formulae of dominant parts of the estimation errors of plant A matrix in PI-MOESP, PO-MOESP, and one of N4SID methods are derived based on a proposing lemma on perturbations to the singular subspaces. By considereing a gap between the true and a perturbed singular subspaces instead of the perturbation on the subspace, a simple formulae of the estimation error is obtained. The order of the subordinate terms are also analysed.


21th International Symposium on Mathematical Theory of Networks and Systems (MTNS)

Explicit formulae of dominant parts of the estimation errors of A and C matrices in PO-MOESP are derived based on a proposing lemma on perturbations to the singular subspaces. The formulae are derived directly from a slightly modified PO-MOESP algorithm. Based on the formulae, the effect of the weighting matrices in the singular value decomposition is analyzed. It is also shown that the original PO-MOESP method is optimal in a sense that singular values of some matrix in the Frobenius norm of the estimation error are minimized.


Waiting Blocked-Tree Type Deadlock Detection

Proc. of Science and Information Conference 2013 (SAI2013)

The Science and Information Organization


Identification of a Hopping Machine Based on a Piecewise Affine Model

The Society of Instrument and Control Engineers

In this study, a hopping machine (HM) is identified as a piecewise affine (PWA) system via estimating a transition time. The HM can be modeled as the PWA model with two types of discrete transitions: one caused by the change between discrete free-fall and spring-mass states and the other caused by inversion of the sign of a nonlinear drag coefficient term owing to air resistance. First, the equation of motion for the HM is obtained considering the nonlinear term in order to analyze those discrete transitions. In addition, the transition times are estimated using a wavelet-based method proposed in our previous papers. Finally, the HM is identified as a piecewise affine auto regressive exogenous (PWARX) model on the basis of the analyzed discrete states.


Asymptotic Properties of MOESP-type Methods

Precision of the estimated plant model is often the main interest of system identification. In order to take the predictable part of the noise into account, the noise model is estimated together with the plant model by using ARMAX model. In that case, a model reduction procedure will be required in order to obtain the plant model. On the other hand, the plant model can be estimated directly by using output error (OE) model. In this paper, PI-MOESP method and PO-MOESP method are compared by analysing the signal and noise components of the estimated plant model under the assumption that there are no common poles in the plant and the noise models. The magnitude of the noise component in each method is discussed when the past or future horizon varies and it is shown that there is a possibility that PI-MOESP method gives better performance than PO-MOESP method.


AUTOMATIC COURSEWARE TRANSFERS BETWEEN DIFFERENT PLATFORMS

Proc. of 6th International Technology, Education and Development Conference (INTED2013)

International Association of Technology, Education and Development


Extensible Web-based Learning Architecture

Proc. of the 2012 International Conference on Software Engineering Research and Practice (SERP'12)


Asymptotic Properties of BCSS Method in Closed Loop Environment

Preprints of the 16th IFAC Symposium on System Identification

The International Federation of Automatic Control

Bias compensated state space model identification method in closed loop environment (CL-BCSS) is introduced. The noise is assumed to be a 0 mean colored noise. It is also assumed that the reference input is a white Gaussian process uncorrelated to the noise. Signal and noise components of a certain matrix in the proposed method and CL-MOESP method are analyzed and compared. It is shown that CL-BCSS method corresponds to CL-MOESP method with infinite past horizon.


Proc. of 6th International Technology, Education and Development Conference (INTED2012)


Tag Command-based Automatic Trouble Diagnosis for Computer Administration

the 5-th International Conference on New Trends in Information Science and Service Science (NISS2011)


Linear Approximation Model for Snake Robots and its Application

This paper proposes a modeling method of snake robots and analyzes the obtained model. Regarding the snake robot as a periodic system, a discrete-time linearized approximation model is obtained based on lifting technique and Fourier series expansion. PI-MOESP type subspace model identification method is applied for obtaining the model. Hankel singular values of the obtained model are analyzed and some remarks are given for the selection of the control input.


A Design of the Scale Parameter of Wavelet Transformation for the Estimation of Transition Time in PWL Systems

This paper proposes a method to determine an appropriate scale parameter of wavelet transform for the estimation of transition time of the Piecewise Linear (PWL) systems by using wavelet analysis. Precision of the estimation of transition time depends on the scale parameter of wavelet transform under noisy environment. Effect of the noise on the estimation of transition time is shown under the assumption of Gaussian random noise in a numerical example. Appropriate range of the scale parameterof wavelet transform is determined by using quantified effect of the noise and effectiveness of the method is illustrated by numerical experiments.


Asymptotic Properties of Bias Compensated State Space Model Identification Method

Preprints of the 18th IFAC World Congress

The authors proposes a bias-compensated state space model identification (BCSS) method in order to relax the order of the persistent excitation (PE) condition. Most of the subspace identification methods utilize instrumental variable (IV) to obtain an unbiased estimate while the proposed method introduces a bias compensation technique to the Ordinary MOESP type method. In this paper, S/N ratios of certain matrices in the proposed method and the PI-MOESP method are analyzed and compared. It is shown that the S/N ratio in the proposed method is better than that in the PI-MOESP method especially when a pole of the plant is near 1 and the number of the block rows of the IV matrix is small.


BCLS Method for the Estimation of Continuous-Time Models in Closed Loop Environment

閉ループ環境における連続時間モデル同定のためのバイアス補償型最小二乗法

Proceedings of 2010 IEEE International Conference on Control Applications

This paper proposes a bias-compensated least squares (BCLS) method for a continuous-time model estimation in closed loop environment. The feedback controller is assumed to be linear time invariant discrete-time system and the feedback system is assumed to be asymptotically stable. It is also assumed that the control input is a zeroth order hold signal. The aim of the proposed method is to reduce the computational cost which will be required for the conventional method.

本論文では閉ループ環境における連続時間モデル同定のためのバイアス補償型最小二乗法を提案する．フィードバック補償器は線形時不変な離散時間系で，閉ループ系は漸近安定とする．また，制御入力は0次ホールドされた信号と仮定する．提案手法の目的は，従来法で必要となった数値計算のコストを減らすことである．


Bias Compensated State Space Identification Method in Closed Loop Environment

A method of bias compensation in subspace identification method in closed loop environment is proposed. The noise is assumed to be a 0 mean colored noise and is assumed to be uncorrelated with the reference input. The covariance matrix of the noise is estimated directly from the residuals instead of estimating the noise model. The proposed method becomes an iterative algorithm and it is analyzed that the proposing method achieves 2nd order convergence.


An Approach to GA-driven Automatic Refactoring based on Design Patterns

2010 Fifth International Conference on Software Engineering Advances

International Academy, Research, and Industry Association


Estimation of Continuous-time Nonlinear Systems by using the Unscented Kalman Filter

This paper proposes a continuous-time model estimation method by using the Unscented Kalman Filter (UKF) from the sampled I/O data, in which the plant parameters as well as the initial state are estimated. The initial state is estimated based on a backward system of the plant, and the parameters are estimated by using the iterated UKF, which repeats the estimation of the forward system and the backward system alternately. In order to demonstrate the effectiveness of the proposed method, the rotary pendulum is provided to estimate the parameters of the continuous-time nonlinear system.


Estimation of a Transition Time in Piecewise Linear Systems

This paper proposes an estimation method of the transition time of piecewise linear (PWL) systems by using wavelet analysis. A definition of detectability of transitions is proposed in advance and its necessary and sufficient condition is presented which gives an insight how to estimate an transition time. The proposing method does not utilize the parameters of the plant model but utilizes a characteristic pattern of the wavelet transformation of the output signal around the transition time.


Bias-Compensated State Space Model Identification

Proceedings of 19th International Symposium on Mathematical Theory of Networks and Systems

A method of bias compensation in subspace identification method is proposed. The noise is assumed to be colored with 0 mean and is assumed to be uncorrelated with the input. The covariance matrix of the noise is estimated directly from the residuals instead of estimating the noise model. The proposing method becomes an iterative algorithm but it converges with order 2.


SERVER-DRIVEN REGRESSION TEST FOR WEB APPLICATIONS AND ITS PERFORMANCE

Proceedings of the IASTED International Conference on Software Engineering


ATTACHABLE SOFTWARE VISUALIZATION FOR MULTIPLE-THREAD PROGRAMS


Synchronization of Multi-window requests for Server-side Regression Test of Web Applications

Proceedings of the 9th International Conference on Quality Software (QSIC2009)


Bias-Compensated Least Squares Method in Closed Loop Environment --- Estimation under colored noise

The Society of Instrument and Control Engineers

In this paper, bias-compensated least squares (BCLS) method is proposed in order to estimate a plant under feedback control when the noise is colored. The feedback controller is assumed to be linear time invariant discrete-time system and the feedback system is assumed to be asymptotically stable. The aim of the proposed method is to reduce the computational cost which will be required for the conventional method.


Customizable Functional Web Components for Visual Web Programming

2009 WRI World Congress on Computer Science and Information Engineering


Web-Based Troubleshooter Generation for System Administration


SERVER-SIDE REGRESSION TEST FOR WEB APPLICATION DEVELOPMENTS


SOFTWARE VISUALIZATION WITH ATTACHABLE DISPLAY CLASSES


Bias Compensation of Recursive Least Squares Estimate in Closed Loop Environment

Proceedings of the 18th International Symposium on Mathematical Theory and Networks and Systems

In this paper, an asymptotic bias of the recursive least squares (RLS) estimate in the closed loop enviornment is analyzed and its compensation method is proposed under the assumption that the noise is white. Namely, a bias compensated RLS method in the closed loop environment based on output error (OE) model is proposed. A posteriori error is also analyzed for the estimation of the noise variance.


Bias-compensated Least Squares Method in Closed Loop Environment

In this paper, a bias-compensated least squares(BCLS) method in the closed loop environment is proposed. It is assumed that the observation noise is a white gaussian signal while there are no process noises. It is also assumed that the plant is controlled by a linear time invariant controller and that the closed loop system is asymptotically stable. The proposed estimator is unbiased and it does not require the reference input to be informative. An iterative redesign of the prefilter is also considered to achieve a minimum variance estimator. The proposed BCLS method is applied for the iterative redesign of the prefilter in order to reduce the computational cost.


Bias-Compensated Least Squares Method in Closed Loop Environment --- A filter design for colored noise

Proceedings of 2008 International Symposium on Advanced Control of Industrial Processes

In this paper, a bias-compensated least squares (BCLS) method in the closed loop environment is extended for the case where the measurement noise is colored. Namely, a BCLS method in the closed loop environment based on Box-Jenkins model is proposed. Noise model is estimated based on a non-parametric model or high order model because the purpose of the closed loop identification is the improvement of the plant dynamics and the purse of the identification of the noise model is to obtain the unbiased plant parameters with smaller variances.


Extensible Syntax-Oriented Verifier and Its Implementation

Proceedings of the IASTED International Conference on Software Engineering


Estimation of the transition time of hybrid systems by using wavelet analysis

Estimation of transition time is important for identification of hybrid systems. This paper proposes an estimation method of the transition time of the hybrid system by using wavelet analysis. Discrete transition of the state can be detected as a characteristic pattern in high frequency range. The effectiveness of the proposed method is illustrated by numerical experiments.


Estimation of Rotary Inverted Pendulum by using the Unscented Kalman Filter --- Estimation of the initial state

This paper proposes a continuous-time model estimation method by using the Unscented Kalman Filter (UKF) from the sampled I/O data, in which the initial state as well as the plant parameter are estimated. The initial state is estimated based on a backward system of the plant. The effectiveness is demonstrated by using the Rotary Inverted Pendulum (RIP) to estimate the initial state and the parameter of a continuous-time nonlinear system.


Bias-compensated adaptive observer for a continuous-time model estimation

Preprints 9th IFAC Workshop Adaptation and Learning in Control and Signal Processing

In this paper, a bias-compensating method for a continuous-time model estimation by using adaptive observer is proposed. It is assumed that the observation noise is a white gaussian signal while there are no process noises. The proposed method is applicable for the identification in the closed loop environment.


Integrated Laboratory Network Management System

Proceedings of the 7th WSEAS International Conference on Applied Informatics and Communications

World Scientific and Engineering Academy and Society


Apty: Easy Enjoyable Effective e-Learning


Bias-compensating method for a continuous-time model estimation by using adaptive observer

Proceedings of European Control Conference 2007

In this paper, a bias-compensating method for a continuous-time model estimation by using adaptive observer is proposed. It is assumed that the observation noise is a white gaussinan signal while there are no process noises. The proposed method is applicable for the estimation of unstable plants in closed loop environment.


Visual Pivot-Table Components for Web Application Development

Proc. of 3rd IASTED International Conference on Advances in Computer Science and Technology


Customizable Verifiers for Web Applications and Their Implementation

Proceedings of International Conference on Computer Engineering and Applications


Rich Component Generation for Web Applications Using Custom Tags


Visual Programming for Web Applications that Use HTML Frame Facilities


Parameter estimation of rotary inverted pendulum based on unscented Kalman filter

The Unscented Kalman Filter (UKF) is a nonlinear estimation method, and has been widely applied in nonlinear control applications. However, if covariance of the initial value is unknown, the estimated value does not converge to the true value in general. To improve the result of estimation, the iterated UKF is propsed as a method to repeat the estimation by redefining the initial value of the filter. Its effectiveness is demonstrated by using the Rotary Inverted Pendulum(RIP) to estimate the parameters of a continuous-time nonlinear system.


Continuous-time model identification by using adaptive observer --- Estimation of the initial state

Importance of the continuous-time model identification is increasing as the computer becomes faster. The main difference between the contiuous-time model identification and the discrete-time identification lies not only in the information loss incurred by sampling but also in that the transient term must be taken into account in the continous-time identification. This paper proposes a continous-time model estimation method from sampled I/O data with the ZOH input assumption. The initial state of the plant is estimated by using a backward system of the plant. An upper bound of the plant degree is assumed to be known. Numerical example shows that the proposed method performs well as the conventional method with an initial state estimation.


Parameter convergence of a continuous-time model estimation method by using adaptive observer

Proceedings of 17th International Symposium onMathematical Theory of Networks and Systems

In this paper, a continous-time model estimation method by using adaptive observer from sampled I/O data with the ZOH input assumption is considered. The proposed method utilizes modified state variable filters in order to estimate the intersample signals. A sufficient condition for the exponential convergence of the parameter estimate is analyzed.


Discrete-time state variable filters for the estimation of a continuous-time model

Proceedings of the 14th IFAC Symposium on System Identification

International Federation of Automatic Control

This paper proposes a continuous-time model estimation method from sampled I/O data with the ZOH input assumption. State variable filters are modified in order to estimate the intersample signals by using a previously obtained parameter estimate. A sufficient condition for the parameter convergence and the bias of the estimated parameters are analyzed.


Continuous-Time Model Identification by using Adaptive Observer --- On the reduction of the computational cost

The Society of Instrument and Control Engineers

The authors have proposed a continuous-time model identification method by using adaptive observer which estimates the intersample output and the initial state of the plant as well as the plant parameters from sampled I/O data. In the previously proposed method, a numerical simulation technique such as Runget-Kutta method is used in order to estimate the intersample signals. However, the continuous-time simulation requires the high computational cost compared to the discrete-time simulation. In this paper, the discrete-time simulation technique is adopted even for the continuous-time state variable filters in order to reduce the computational cost.


Continuous-Time Model Identification using Adaptive Observers --- On the Selection of State Variable Filters

Proceedings of the 24th IASTED International Conference Modelling, Identification, and Control


A sufficient condition for the convergence of adaptive observer in continuous-time model identification

Proceedings of IFAC Workshop on Adaptive and Learning in Control and Signal Processing

This paper proposes a new method to identify a continuous-time model from sampled I/O data by using an adaptive observer. The boundedness of the parameter estimate and the exponential convergence of the parameter estimation error to 0 under the assumption of Persistently Exciting(PE) signals are guaranteed. In order to identify the plant from a finite number of I/O data, an adaptive observer for a backward system is also proposed. In noise-free cases, the magninude of the parameter estimation error can be made arbitrarily small if the sampling period is smaller than a certain value.


Continuous-Time Model Identification by using Fast Adaptive Observer

Proceedings Sixteenth International Symposium on Mathematical Theory of Networks and Systems

This paper introduces a new method to identify a continuous-time model from sampled I/O data by using an adaptive observer. The adaptive algorithm is based on Newton-like method that the convergence of the parameter is considereably improved compared to the steepest descent method. In order to identify the plant from a finite number of I/O data, an adaptive observer for a backward system is also proposed.


Multi-objective Design Scheme for Robust I-PD Controller

Proc. of the 23rd IASTED International Conference on Modelling, Identification, and Control

筆者の一人は, 速応性と振動抑制性を考慮して, 一般化ISEに基づく制御系設計手法を提案している. 一般化ISEは, 出力誤差の2乗積分と出力誤差の微分値の2乗積分の和である. それぞれの項は, 速応性と振動抑制性に関係している. ところが, それらの項をどのような割合で足したものが評価関数としてよいのかは, 事前にはわからない. そこで, 本論文では, それぞれの項の最小化する2目的問題として定式化し, 速応性と振動抑制性のトレードオフをとる設計手法を提案している.


Continuous-Time Model Identification By Using Adaptive Observer --- A case of non-uniform sampling

Proc. of the 23rd IASTED International Conference Modelling, Identification, and Control

This paper proposed a continuous-time model identification from sampled I/O data by using an adaptive observer in which the output is non-uniformly sampled. The boundedness of the parameter estimate error and the exponential convergence of the parameter estimate error to 0 under the PE assumption are guaranteed. In order to identify the plant from a finite number of I/O data, an adaptive observer of a backward system is also proposed.


Continuous-time model identification by using adaptive observer

Preprints of 13th IFAC Symposium on System Identification

International Federation of Automatic Control

This paper proposes a continuous-time model identification from sampled I/O data by using an adaptive observer. The boundedness of the parameter and the exponential convergence of the parameter estimate error to 0 under the PE assumption are guaranteed. In order to identify the plant from a finite number of the I/O data, an adaptive observer of a backward system is also proposed.


An Approach to Brain-Image Oriented Programming for Web Applications

Proc. of 7th World Multiconference on Systemics, Cybernetics and Informatics

ユーザが頭に描いたイメージを基にして，プログラムを開発でき，プログラムを構成するコンポーネントの完備性や，コンポーネント間の関係の一貫性をユーザが容易に検証でき，___ 設計の途中でも，ユーザがそれまでに設計したコンポーネントの実行結果を容易に確認できるという特徴をもつイメージ指向プログラミング方式について提案し，アプリケーションを対象として本方式を実現したシステムについて述べる．


Design of robust I-PD controller based on Pareto-optimality with generalized ISE

Proceedings of the 22nd IASTED International Conference on MODELLING, IDENTIFICATION, AND CONTROL

先の論文では, 速応性と振動抑制性の2目的最適化問題を, 閉ループ系の極の実部の最大値の最小化, および, 原点から見た虚軸からの角度の最大値の最小化という2つの最小化問題に置き換えて, 2目的問題を解いた. 一方, 筆者の一人はロバスト性を考慮した上で, 速応性と振動抑制性の両方を考慮した制御系設計手法として一般化ISEという評価指標に基づく手法を提案している. そこで, 本論文では, 二つの手法の特徴を実際の磁気浮上系を例題にとり, 比較·検討した.


Adaptive Observer of Sampled-Data Systems

Proceedings of the 22nd IASTED International Conference MODELLING, IDENTIFICATION, AND CONTROL

与えられたサンプル値入出力データから対象の連続時間モデルを直接同定する適応観測器を提案し, その収束性いついて解析した. サンプル点間の応答を得られたプラントモデルに基づいて推定し, また, 推定された連続時間応答からプラントモデルを推定するという繰り返し型のアルゴリズムである.


Design of robust I-PD controller based on multi-objective genetic algorithm with local search techniques

Proceedings of the International Symposium on Nonlinear Theory and Its Applications

先に提案したロバストI-PD制御器設計法は, 速応性と振動抑制性を制御系の整定時間と減衰比の改善問題と捉え, さらに, 制御系の極配置問題に置き換えてから遺伝的アルゴリズムによって最適解を求めるという手法である. 最適解探索アルゴリズムにおいて, 収束性を改善するために, 局所探索手法を導入した.


Identification of the jump in switched linear systems

Proceedings of the International Symposium on Nonlinear Theory and Its Application

ハイブリッドシステムの一種である区分線形システムの動特性を同定する場合, 連続的な動特性だけでなく, 離散的な状態遷移についても同定する必要がある. 本論文では, 離散的な状態遷移が起こる時刻は既知として, 離散的な状態遷移と連続的な動特性を同時に推定する手法を提案し, 数値例によって, 有効性を検証した.


A Design of Robust I-PD Controller Based on Genetic Algorithm

Proceedings of the IASTED International Conference on MODELLING, IDENTIFICATION, AND CONTROL

The International Association of Science and Technology for Development

A New design methos of robust I-PD controller based on Genetic Algorithm(GA) is presented. It is defined as a two-objective optimization problem in which an issue of settleing time and damping ratio is translated into a pole assignment problems. The uncertainty of the plant is represented as a polytope of polynomials and a design cost is reduced by using the edge theorem.


Limit Cycle in Fault Tolerant Decentralized Adaptive Control System

Proceedings of 2000 International Symposium on Nonlinear Theory and its Applications

筆者らが提案している自律分散適応制御において, あるサブシステムが故障したとき, 解軌道のリミットサイクルが, 故障後に別のリミットサイクルに変形することによって, 耐故障性が達成されていることを示した.


Autonomous Decentralized Adaptive Control System Using Parameter Projection

筆者らが提案している分散型ハイゲイン適応制御は, パラメータ調整則にσ修正法を用いているため, 雑音や外乱が存在しなくても, 出力が0には収束しない場合がある. そこで, パラメータがある範囲内ならばσ修正項を無効にする調整則を提案し, 安定性の解析, 耐故障性を示した. また, σ修正項が無効となる範囲を適切に選ぶことにより, 出力の0への収束性を数値例により確認した.


Adaptive Stabilization of Any Minimum Phase Plant with Relative Degree n or n+1

Preprints of the 11th IFAC Symposium on System Identification

正実関数には+1,0,-1の相対次数に関する自由度があることを利用して相対次数が正確にわかっていなくても適応的に安定化する適応制御を提案した. 相対次数が-1の伝達関数は非因果的となるため, 本論文では 0 と+1 に相当する場合について安定性を証明した.


Fault Tolerant Decentralized Adaptive Control Systems Using Backstepping

Proc. of the 34th IEEE Conf. on Decision and Control

Backstepping 法を用いた分散型ハイゲイン適応制御系をサーボ問題へ拡張した. これによって, ステップ目標値, 正弦波, ランプ目標値などへ対応できるようになり, また, 各サブシステムの相対次数がnである場合へ適用可能となった. 耐故障性やオンライン拡張性などのよい性質もそのまま成り立つ.


Decentralized High Gain Adaptive Control System Using Backstepping

Proc. of the 3rd IEEE Symposium on Emerging Technologies and Factory Automation

筆者らが提案している分散型ハイゲイン適応制御は各サブシステムの相対次数が1であるという制約条件が必要であったが, この制約をbackstepping法を用いることによって緩和した.


Simultaneous Stabilization by Periodically Switching Control

同一の線形補償器では安定化不可能な2つの対象が与えられたとき, 同じアルゴリズムに基づく制御によって安定化する, 同時安定化問題を考えた. 本論文では, 周期時変補償器を提案し, 安定性の解析を行った.


Autonomous Decentralized Adaptive Control

Proc. of IEEE 2nd International Workshop on Emerging Technologies and Factory Automation

本論文では, 筆者らが提案している分散型ハイゲイン適応制御は耐故障性を持つことを自己相似性に基づいて示し, 分散型制御系の特徴である, 耐故障性, オンライン拡張性などを説明する原理を明らかにした.


A Servo Design of Decentralized Adaptive Control

Proc. of International Symposium on the Mathematical Theory of Networks and Systems

先に提案した分散型ハイゲイン適応制御では, 制御系の安定化を目的としていたが, 本論文では, 一定目標値などへの追従を制御目的としたサーボ系の構成へ拡張した. すべての信号の有界性と, 耐故障性の解析を行っている.


Fault Tolerance of Decentralized Adaptive Control

Proc. of International Symposium on Autonomous Decentralized Systems

分散型制御系の特徴として, 耐故障性, 保守·点検の容易さ, オンライン拡張性などが挙げられている. 本論文では, 分散型ハイゲイン適応制御の安定性解析に基づき, 耐故障性を持つことを自己相似性に基づいて示した.


A Method of Fuzzy Control Using a Probabilistic Model

Proc. of the Korea-Japan Joint Conference on Fuzzy Systems and Engineering

ファジィ制御は, ファジィ集合を使わずとも, 確率モデルを用いて構成可能であると思われていたが, これは証明されたことではなかった. 本論文では, まず, この予想が正しいことを示した上で, ファジィ制御の方が確率的手法に比べて使いやすいこと, および, 確率的手法では確率密度関数に関する完全な知識が必要である点を指摘した.


Numerical analysis of state-term estimation in closed-loop LPV identification

閉ループLPV同定における状態項に関する数値的解析

第10回計測自動制御学会制御部門マルチシンポジウム

第10回計測自動制御学会制御部門マルチシンポジウム

The Society of Instrument and Control Engineers

This paper considers numerical analysis of state-term estimation in a closed-loop LPV (linear Parameter-Varying) identification method presented by the authors. Based on predictor-based system identification (PBSID) for an LTI (Linear-Time-Invariant) system, computation of the intersection between the pas and future data is motivated. The closed-loop LPV identification method presented by the authors is moreover reviewed, and the past and future part of the Gram matrices are derived. A computation method of the intersection between them is presented.


Frequency Domain Uncertainty Estimation in a Closed Loop Subspace Identification Method

ある閉ループ部分空間同定法における周波数領域での不確かさの推定

第10回計測自動制御学会制御部門マルチシンポジウム

第10回計測自動制御学会制御部門マルチシンポジウム

This paper proposes an estimate of the covariance matrix of the estimated system matrices in a closed-loop identification method previously proposed by the authors. The estimate of the covariance matrix can be derived from a set of input/output data used for the identification. It is important to provide a covariance matrix of the estimate in order to guarantee the quality of the estimate. Based on the estimated covariance matrix, the uncertainty of the estimated transfer function is derived. Numerical simulations illustrate the validity of the derived covariance estimate.


Identification of Nonlinear ARX model using kernel method with random Fourier features

乱択化フーリエ特徴関数によるカーネル法を用いた非線形ARXモデルの同定

第65回自動制御連合講演会

第65回自動制御連合講演会

This paper studies identification of nonlinear-ARX (NARX) model using a kernel method. Random Fourier features are applied to identification of a NARX model, since the size of the matrix in computing regression becomes large in kernel methods, and the numerical computation time is long. Numerical simulation is given to evaluate effectiveness of the present method.


Covariance estimation of the estimated extended observability matrix in Closed-Loop MOESP

Closed-Loop MOESP法における拡大可観測性行列推定値の共分散行列の推定

第65回自動制御連合講演会

第65回自動制御連合講演会

It is important to provide a covariance of the estimates to ensure the quality of the identification results. This paper proposes a formula of the covariance of the estimate of the extended observability matrix in Closed-Loop MOESP. The derived formula uses the gap between singular subspaces to estimate the perturbation of the extended observability matrix. The derived formula can be used to estimate its covariance matrix from a single identification result. Numerical simulation compares the sample covariance with the derived formula


On state estimation of LPV system under feedback via kernel method

カーネル法による閉ループ下にあるLPVシステムの状態推定について

第9回計測自動制御学会制御部門マルチシンポジウム

第9回計測自動制御学会制御部門マルチシンポジウム

This paper studies state-space estimation for identifying a Linear Parameter Varying (LPV) system. By using kernel least-squares and kernel canonical correlation analysis, two state-space identification algorithms for a Linear-Time-Invariant (LTI) system are extended: The one is PBSID (Predictor Based System IDentification), and the other is the stochastic realization or stochastic subspace identification. It is pointed out in this paper that a closed-loop state-space identification algorithm for an LPV system recently developed coincides with the one obtained by extending the stochastic realization algorithm to an LPV system. Numerical simulations show that the algorithm based on stochastic realization suffers from numerical problems and that the algorithm based on PBSID outperforms that of stochastic realization.


Numerical Analysis on the Rank of Weighting Matrix in BLUE

BLUEにおける重み行列のランクに関する数値解析

第9回計測自動制御学会制御部門マルチシンポジウム

第9回計測自動制御学会制御部門マルチシンポジウム

This paper numerically analyzes the rank of a weighting matrix in the BLUE (best linear unbiased estimator), which was previously proposed by the authors for the estimation of noise covariance matrix of the system. Since the weighting matrix is singular, its rank is required for the calculation of the pseudo-inverse. The rank of the weighting matrix is numerically analyzed and it is given as a function of the system degree, the number of output, and a design parameter called future horizon.


Idetification of minimal realization of plant under feedback --- an approach via state estimation

フィードバック下にある同定対象の最小実現の同定 --- 状態推定によるアプローチ

第64回自動制御連合後援会

第64回自動制御連合後援会

This paper formulates a problem of finding a minimal realization of an unstable plant under feedback. Different from the earlier studies by the authors, the problem setting does not assume a linear-time-invariant controller of the finite order. The authors introduce an orthogonal direct sum decomposition of the input-output data in a Hilbert space and derive a state estimation method for a minimal realization of the plant based on the decomposition. They develop a realization algorithm for finding a minimal realization of an unstable plant based on the result and obtain a closed-loop subspace identification method.


On the covariance analysis of the estimated of the noise covariance

雑音共分散行列の推定値の分散解析

第64回自動制御連合後援会

第64回自動制御連合後援会

This paper analyzes the covariance of the estimated covariance matrix of the process and measurement noises in Gauss-Markov models. From some numerical simulations, it was shown that an estimation method of the noise covariance with CCA (canonical correlation analysis) weighting matrix proposed by the authors gives good performance. The covariance of the error term in the proposed method is derived. On the other hand, a BLUE (best linear unbiased estimate) for the corresponding problem is derived by using a newly derived lemma. The proposed estimate is compared with the BLUE.


Minimal realization of unstable system under feedback --- Derivation of numerically sound calculation method

閉ループ下にある不安定系の最小実現 --- 数値的に安定な計算法の導出

第8回計測自動制御学会制御部門マルチシンポジウム

第8回計測自動制御学会制御部門マルチシンポジウム

This paper develops a numeirically sound method for identifying a minimal realization of an unstable linear plant under feedback. It is assumed that the plant and noise models have non-common state matrices and that the plant is stabilized by a linear time invariant system of finite order. A numerically sound computation method is derived by the product of the extended observability and reachability matrices of the left and right coprime factor representations of the plant, by improving the past research of the authors. Numerical simulation results show the effectiveness of the proposed method.


Numerical Analysis of the Reduction of the Bias in Closed-Loop Subspace Identification Method

閉ループ部分空間同定法におけるバイアス低減に関する数値的解析

第8回計測自動制御学会制御部門マルチシンポジウム

第8回計測自動制御学会制御部門マルチシンポジウム

It is seen that the estimate of the extended observability matrix in Closed-Loop MOESP has a smaller bias compared to other methods when pas/future horizons are comparatively small. The reason of the fact is that the image of the term causing the bias is close to the image of the extended observer matrix. In this paper, it is analyzed theoretically and numerically how close the images are and numerical simulation shows that the bias is reduced in practice.


Selection of Input Joints for Gait Generation of Biped Robot

二足歩行ロボットの歩容生成における入力関節の選択について

第7回 制御部門マルチシンポジウム

第7回 制御部門マルチシンポジウム

エネルギー制御を用いて二足歩行ロボットの歩容生成をするとき，加えるエネルギーが同じでも入力する関節やタイミングが異なると異なる歩容が生成される．入力トルクによってつぎ込むエネルギー量が変わる．そのためトルクを入力する関節を選択しなければならない．本研究では，効率の良い歩容生成を目的とし，エネルギー制御によって求めたトルクを入力する関節の選択について考察した．検証として，腰に入力した場合と膝に入力した場合を比較した．その結果，エネルギー効率の良い入力関節が膝であることがわかった．


Estimation of parameters using system identification and application to robust nonlinear estimation

システム同定を用いた物理パラメータの推定と非線形ロバスト推定手法への適用

第7回 制御部門マルチシンポジウム

第7回 制御部門マルチシンポジウム

In this study, we propose an estimation method of physical parameters of the plants and their uncertainty of the controlled object using the system identification and Unscented Transformation (UT). By accurately estimating the uncertainty, the deterioration of the precision of the state estimate is expected to be reduced.


ドローンのエネルギー消費のモデル化

第62回自動制御連合講演会

第62回自動制御連合講演会

The Institute of Systems, Control and Information Engineers

In this paper, we focus on drone's battery exhaustion. The purpose is to extend the flight time by controlling the flight speed. We obtain electric current data using amperometer on the drone. Next, we estimate a model of energy consumption using system identification an compare the energy consumption of the real machine and the estimated model.


Identification of systems having unreachable stochastic system with eigenvalues on the closed unit disc

不可到達な確率系をもつ安定限界なシステムの同定

第6回 制御部門マルチシンポジウム

第6回 制御部門マルチシンポジウム

This paper applies an identification method of innovations model previously proposed by the authors to the case when the system is unstabilizable. It is shown that the previously proposed optimization problem can estimate the noise covariance even when the system is unstabilizable. The step of estimating the innovations model from the noise covariance is modified so as to treat the unstabilizable systems. A numerical simulation illustrates the effectiveness of the proposed method.


Another semidefinite programming problem for subspace identification method taking initial state into account

初期状態を考慮した部分空間同定法のためのもう一つの半正定値計画問題

第61回自動制御連合講演会

第61回自動制御連合講演会

This paper proposes an another semi-definite programming problem for the consistent estimation of the noise covariance and analyzes the solvability of the problem. The authors proposed two methods: one is to estimate the noise covariance together with the bias term, and the other is to estimate the noise covariance by cancelling the bias term. This presentation concerns the latter approach. The merit of the proposed method is the size of the data matrix required in the proposed method is smaller than that in the previous method. Conditions for the problem to be solved and the uniqueness of the spectral density function of the noise are analyzed.

本論文は雑音分散の一致推定のためのもう一つの半正定値計画問題を提案し，その可解性について解析する．筆者らは二つの手法を提案してきた．一つは雑音分散とバイアス項を同時に推定する手法，もう一つは，バイアス項を消去してから雑音分s何を推定する手法である．本論文の手法は後者に属する．提案手法の利点は以前に提案した手法に比べ，必要とされる行列のサイズが小さくて済むことである．問題が可解となるための条件や雑音のスペクトル密度関数が一意に推定されることを解析する．


On the Identifiability in an SDP Problem for the Estimation of Innovations Model

イノベーションモデル推定のためのSDP問題における可同定性について

第5回 制御部門マルチシンポジウム

第5回 制御部門マルチシンポジウム

The authors proposed an SDP formulation for the identification of the Kalman gain and the covariance matrix of the innovations process, aiming at a consistent estimation. However, the rank of a coefficient matrix of the variables does not have a column full rank, so that the uniqueness of the solution or the identifiability are not clear. In this paper, a sufficient condition for the system to be identifiable by the proposed method almost everywhere is derived.

筆者らはカルマンゲインとイノベーション過程の共分s何行列の一致推定を目指して，半正定値計画問題を提案してきた．しかしながら，最適化変数の係数行列は列フルランクとならないため，問題の可解性や同定可能性は不明であった．本論文では，ほとんど至るところでシステムが同定可能となるための十分条件を導出する．


Stochastic subspace identification methods taking initial state into account via semidefinite programming approach

半正定値計画問題により初期状態を考慮した確率部分空間同定法

第60回自動制御連合講演会

第60回自動制御連合講演会

In this paper, the authors develop stochastic subspace identification algorithms taking initial state into account via a semidefinite programming approach. They develop two subspace algorithms by deleting the initial state from the covariance matrix and by finding the white-input and the state terms in the covariance data. They moreover consider variations of the computation algorithms based on the minimum and maximum convariance matrix of the state of the stochastic system. They provide numerical simulation, comparing the algorithms with the one previously proposed.

本論文では，半正定値計画問題を用いて初期状態を考慮した確率部分空間同定アルゴリズムを開発する．一つは共分散行列から初期状態を消去する手法，もう一つは共分散データの白色雑音成分と状態項をともに推定する手法である．さらに確率システムの状態の最大共分散と最小共分散にもとづく計算アルゴリズムを考える．以前に提案した手法とこれらの手法を数値シミュレーションで比較する．


Problem Formulation for Estimating Innovations Model

変数変換を用いたイノベーションモデル推定問題の定式化

第60回自動制御連合講演会

第60回自動制御連合講演会

An estimation problem of kalman gain and the covariance of the innovations process has been formulated as a nonlinear optimization problem in order to reduce the bias of the estimate. In this paper, the problem is formulated as an SDP (semi-definite programming) problem by using change of variables. The effectiveness of the proposed method is illustrated by using a numerical example.

カルマンゲインとイノベーション過程の共分散行列の一致推定は非線形最適化問題として定式化されてきた．本論文では，変数変換の手法を用いることで，同定問題を半正定値計画問題として定式化する．提案手法の有用性を数値例により示す．


Closed-loop identification based on the null-space of the extended observability matrix

拡大可観測行列の零空間に基づく閉ループ同定

The 4th Multi-symposium on Control Systems (MSCS 2017)

第4回制御部門マルチシンポジウム

The Society of Instrument and Control Engineers

In this paper, the authors consider closed-loop identification based on a stochastic system for the joint-input-output. They develop a new closed-loop system identification method based on the null-space of the extended observability matrix for the stochastic system by taking the initial state of the stochastic system into account. Numerical simulation indicates that the new method gives better results than the closed-loop system identification method developed earlier by the authors based on a Riccati equation and linear matrix inequalities to guarantee positive realness of the stochastic system.

本論文では，確率システムの結合入出力に基づいた閉ループ同定を考える．確率システムの初期状態を考慮に入ることによって確率システムの拡大可観測行列の零空間に基づく新しい閉ループ同定手法を提案する．数値シミュレーションにより，新しい提案手法は筆者らが以前に提案した確率システムの正実性を保証するリカッチ方程式とLMIに基づく閉ループ同定法よりも良い結果を与えることを示す．


Local Convergence of an Algorithm for Consistent Estimate of Innovations Model

イノベーションモデルの一致推定のための解法の局所収束性

第4回 制御部門マルチシンポジウム

第4回 制御部門マルチシンポジウム

This paper analyzes the convergence property of a numerical algorithm proposed by the author, which estimates Kalman gain and the covariance matrix of a innovations process consistently. The estimates are given as solutions of some BMI problem, so an algorithm which iteratively solves some LMI's are proposed. In this paper, the algorithm is shown to be locally convergent.

本論文は，筆者が以前に提案したカルマンゲインとイノベーション過程の共分散行列の一致推定を与える数値アルゴリムズの収束性について解析する．推定値はあるBMI問題の解として与えられるため，LMIを繰り返し解く手法が提案される．本論文では，提案されたアルゴリズムは局所的に収束することを示す．


Convergence of an Estimation Method of kalman Gain by using Cholesky Factor

Cholesky Factor を用いたKalmanゲインの推定手法の収束性について

第59回自動制御連合講演会

第59回自動制御連合講演会


Consistent Estimate of Kalman Gain by using Cholesky Factor

Cholesky 分解を用いた Kalman ゲインの一致推定

第60回システム制御情報学会研究発表講演会

第60回システム制御情報学会研究発表講演会


Numerical Solution for a Consistent Estimate of kalman Gain

カルマンゲインの一致推定値の数値的解法

第3回制御部門マルチシンポジウム

第3回制御部門マルチシンポジウム


Consistent Estimate of Covariance Matrix of Innovations Process

イノベーション過程の共分散行列の一致推定値

第58回自動制御連合講演会

第58回自動制御連合講演会

The Institute of Systems, Control and Information Engineers


Error Analysis of Subspace Identification Method in Closed Loop Environment

閉ループ環境における部分空間同定法の誤差解析

第59回システム制御情報学会研究発表講演会

第59回システム制御情報学会研究発表講演会

This paper analyzes the estimation error of B and D matrices in closed-loop environment which are estimated by using OE-PIV method proposed by Chou and Verhaegen. Estimates of A and C matrices in closed-loop environment which are obtained by applying PO-MOESP method in the framework of direct method of the closed loop identification has no asymptotic bias and their estimation error can be derived in the previous paper. In this paper, the relation between the estimation error of (B,D) and the estimation error of (A,C,K) are derived.


Error Analysis of PO-MOESP method in Closed Loop Environment --- Analysis on the estimates of A and C matrices

閉ループ環境におけるPO-MOESP法の誤差解析 --- A, C行列の推定値の解析

The 2nd Multi-symposium on Control Systems

第2回制御部門マルチシンポジウム


Error Analysis of PO-MOESP method in Closed Loop Environment --- Analysis of the estimates of A and C matrices

閉ループ環境におけるPO-MOESP法の誤差解析 --- A, C 行列の推定値の解析

第2回制御部門マルチシンポジウム

第2回制御部門マルチシンポジウム

This paper analyzes the estimation error of A and C matrices in PO-MOESP method in closed loop environment. The estimates of B and D in PO-MOESP method will be biased in closed loop environment while the estimates of A and C will be unbiased. So, PO-MOESP method is still applicable for the estimation of A and C matrices in closed loop environment. In the previous analysis, the plant is assumed to be stable, and there are error terms which can be neglected when the future horizon is set to be large. In the closed loop environment, the plant might be unstable and the term will not be negligible. In this paper, the error terms are analyzed in detail and their explicit formulations are derived.


Error Analysis of B and D matrices in PO-MOESP Method

PO-MOESP法における B, D 行列の推定誤差解析

第57回自動制御連合講演会

第57回自動制御連合講演会

Estimation errors of B and D matrices in PO-MOESP method are analyzed. The gap related amount plays an important role in the analysis of the estimation errors. Not only the most dominant terms but also the orders of the subordinate terms of the estimation errors are analyzed.


Comparison of Shift Invariance/State Approaches in Subspace Identification Methods

部分空間同定法におけるシフト不変/状態アプローチの比較

第58回システム制御情報学会研究発表講演会

第58回システム制御情報学会研究発表講演会


第1回制御部門マルチシンポジウム

第1回制御部門マルチシンポジウム

The Society of Instrument and Control Engineers


On the precision of the plant estimates based on OE/ARMAX models

第56回自動制御連合講演会

第56回自動制御連合講演会

Identification method based on ARMAX model gives better performance when the system is contaminated by a colored noise because noise model as well as plant model is estimated. However, in order to obtain an plant model, some process such as model reduction or decoupling will be required. Thus, the influence of such process to the precision of the plant model must be analyzed. This paper analyzes the estimation error in $A$ matrix of the plant model when OE model and ARMAX are adopted.


Energy-recovery control of bipedal walking robot

二足歩行ロボットのエネルギー回生制御

2013年度 計測自動制御学会四国支部学術講演会

2013年度 計測自動制御学会四国支部学術講演会

受動歩行は, 人間の歩行のように自然で滑らかな歩行であるため, エネルギー効率に優れていると考えられ注目されている. しかし, 受動歩行を行うと二足歩行ロボットは膝が伸びきったり, 地面に脚をついたときに運動エネルギーが減少している. このエネルギーを回生し, さらにエネルギー効率の良い歩行を実現する.


Stabilization of the bipedal walking robot using orthogonal function expansion

2013年度 計測自動制御学会四国支部学術講演会

2013年度 計測自動制御学会四国支部学術講演会

受動歩行を行う二足歩行ロボットは制御を必要としない代わりに小さい外乱で倒れてしまう. そこで, 非線形システムを周期システムとみなし, 直交関数展開を用いて制御を行うことで倒れやすいパターンでの転倒を防ぐ.


Web アプリケーションのデバッグ・保守支援

Web アプリケーションのデバッグ・保守支援

電気関係学会四国支部連合大会

電気関係学会四国支部連合大会


Variance analysis of plant model using MOESP type methods

MOESP法におけるプラントモデルの分散解析

第57回システム制御情報学会研究発表講演会

第57回システム制御情報学会研究発表講演会

Precision of the estimated plant model is often the main interest of system identification. In order to take the predictable part of the noise into account, the noise model is estimated together with the plant model by using ARMAX model. In that case, a model reduction procedure will be required in order to obtain the plant model. On the other hand, the plant model can be estimated directly by using output error (OE) model. In this paper, PI-MOESP method and PO-MOESP method are compared by analysing the signal and noise components of the estimated plant model under the assumption that there are no common poles in the plant and the noise models. The magnitude of the noise component in each method is discussed when the past or future horizon varies and it is shown that there is a possibility that PI-MOESP method gives better performance than PO-MOESP method. A numerical example is given to illustrate the discussions.


Linear Approximation Model for Bipeds Robot and its Application

SICE 13th Annual Conference on Control Systems

第13回計測自動制御学会制御部門大会

Modeling method of a biped robot by using periodic property is proposed. The input and the output of the robot is sampled by using lifting technique and discretized by expanding the sampled signals by using orthonal functions. The coefficients of the sampled input signal are restricted to be of finite dimension while the coeeficients of the output signal are projected to a finite dimensional set. In order to estimate the linear approximation model, a subspace identification method is utilized.


Asymptotic Variance Analysis of Bias Compensated State Space Model Identification Method

バイアス保証型部分空間同定法の漸近的分散解析

第55回自動制御連合講演会

第55回自動制御連合講演会

The Institute of Systems, Control and Information Engineers

Based on the analysis of asymptotic variance in bias compensated state space model identification(BCSS) method, PI-MOESP method and PO-MOESP method are compared under the condition that there are no common poles in plant model and noise model. It is shown that there is a possibility that PI-MOESP method gives better performance than PO-MOESP and a numerical example is given to show such case.


サーバベースWeb アプリケーション回帰統合テスト支援方式

サーバベースWeb アプリケーション回帰統合テスト支援方式

電気関係学会四国支部連合大会

電気関係学会四国支部連合大会


Waiting ブロック・ツリー型デッドロックの検出

Waiting ブロック・ツリー型デッドロックの検出

電気関係学会四国支部連合大会

電気関係学会四国支部連合大会


Construction of Stock Trading Support System Using Neural Network

ニューラルネットワークを用いた株売買支援システムの構築

第56回システム制御情報学会研究発表講演会講演論文集

第56回システム制御情報学会研究発表講演会講演論文集


Forecasting System of Actual Temperature and Humidity Using Stochastic Real-Valued Unit

Stochastic Real-Valuedユニットを用いた気温・湿度予測システムの構築

第56回システム制御情報学会研究発表講演会講演論文集

第56回システム制御情報学会研究発表講演会講演論文集


Autonomous Transfer System in Environment Which Have Irregular Moving Obstacles

不規則に動く障害物が存在する環境での自律移動システムの開発

第56回システム制御情報学会研究発表講演会講演論文集

第56回システム制御情報学会研究発表講演会講演論文集


Autonomous Cooperation System of Mobile Objects Using Stochastic Real-Valued Unit

実数値出力学習ユニットを用いた複数移動体の自律協調システム

第56回システム制御情報学会研究発表講演会講演論文集

第56回システム制御情報学会研究発表講演会講演論文集


Asymptotic Properties of Bias Compensated State Space Model Identification Method in Closed Loop Environment

SICE 12th Annual Conference on Control Systems

第12回計測自動制御学会制御部門大会

Bias compensated state space model identification method in close loop environment (CL-BCSS) is introduced. The noise is assumed to be a 0 mean colored noise. It is also assumed that the reference input is a white Gaussian process uncorrelated to the noise. Signal and noise components of a certain matrix in the proposed method and CL-MOESP method are anlyzed and compared. It is shown that CL-BCSS method corresponds to CL-MOESP method with infinite past horizon.


Development of a victim search system by using autonomous learning unit

自律学習ユニットを用いた被災者探索システムの開発

第20回計測自動制御学会中国支部学術講演会論文集

第20回計測自動制御学会中国支部学術講演会論文集


ニューラルネットワークを用いた株価短期変動レンジ予測システムの構築

ニューラルネットワークを用いた株価短期変動レンジ予測システムの構築

2011年度計測自動制御学会四国支部学術講演会論文集

2011年度計測自動制御学会四国支部学術講演会論文集


スイッチング非定常環境下での学習ユニットによる未知大域的最適解探索

スイッチング非定常環境下での学習ユニットによる未知大域的最適解探索

2011年度計測自動制御学会四国支部学術講演会論文集

2011年度計測自動制御学会四国支部学術講演会論文集


Variance Analysis of Bias Compensated State Space Model Identification Method --- Case of 1st order system

バイアス補償型部分空間同定法の分散解析 --- 1次系の場合

第40回制御理論シンポジウム資料

A method of bias compensation in subspace identification method is introduced and an analysis on its variance is presented. The noise is assumed to be a 0 mean colored noise and is assumed to be uncorrelated with the input. The variance of the extended observability matrix is analyzed and a closed form of the variance is derived for the case when the plant is a 1st order system and the noise is white.


An Approach to Design Pattern-Based Refactoring

デザインパターンに基づくソフトウェア・リファクタリング方式の提案

電気関係学会四国支部連合大会

電気関係学会四国支部連合大会


Construction of Autonomous Sailing System of Ship with SRV Learning Unit

SRVユニットを用いた船舶自律航行システムの構築

Proceedings of the 55th Annual Conference of the Institute of Systems, Control and Information Engineers

第55回システム制御情報学会研究発表講演会講演論文集


Forecasting System of Actual Sunshine Duration based on Weather Statistics Information

気象統計情報に基づく日照時間予測システムの構築

第55回システム制御情報学会研究発表講演会講演論文集


Short-term Change Prediction of Stock Prices by Using Neural Network

ニューラルネットを利用した株価の短期変動予測

第55回システム制御情報学会研究発表講演会講演論文集


Global Optimal Solution Search under Unknown Switching Environment

スイッチング未知環境下での大域的最適解探索

第55回システム制御情報学会研究発表講演会講演論文集


Variance Analysis of Bias Compensated State Space Model Identification Method

バイアス補償型部分空間同定法の分散解析

SICE 11th Annual Conference on Control Systems

第11回計測自動制御学会制御部門大会

A method of bias compensation in subspace identification method is introduced and an analysis on its variance is presented. The noise is assumed to be a 0 mean colored noise and is assumed to be uncorrelated with the input. The variance is analyzed from a view point of S/N ratio of a matrix before the singular value decomposition and then perturbation on the left singular subspace is analyzed.


Bias-Compensated State Space Identification Method --- A Numerical Analysis of the Gap between the Singular Subspaces

バイアス補償型部分空間同定法 --- 特異部分空間の開きの数値解析

第11回適応学習制御シンポジウム資料

第11回適応学習制御シンポジウム資料

A method of bias compensation in subspace identification method is introduced and a numerical analysis of its variance is presented. The noise is assumed to be a 0 mean colored noise and is assumed to be uncorrelated with the input. The variance is analyzed from a view point of S/N ratio of a matrix before the singular value decomposition. However, the numerical analysis reveals that the gap between the nominal and perturbed singular subspaces is slightly different from the S/N ratio of the matrix.


Stock buying and selling prediction using neural network and stock automatic buying and selling system

ニューラルネットワークを用いた株売買予測とそれに基づく株自動売買システム

2010年度計測自動制御学会四国支部学術講演会論文集

2010年度計測自動制御学会四国支部学術講演会論文集


Construction of Stochastic Estimator-Type Hierarchical Structure Learning Algorithm and Its Preformance

Stochastic Estimator型可変階層構造学習アルゴリズムの構築とその性能

2010年度計測自動制御学会四国支部学術講演会論文集

2010年度計測自動制御学会四国支部学術講演会論文集


Construction of Autonomous Transfer System for Rescue by Plural Movement Bodies

自律移動体を用いた災害被災者探索システムの構築

2010年度計測自動制御学会四国支部学術講演会論文集

2010年度計測自動制御学会四国支部学術講演会論文集


Property of Stochastic Real-Valued Unit in Noisy Environment

2010年度計測自動制御学会四国支部学術講演会論文集

2010年度計測自動制御学会四国支部学術講演会論文集


Numerical Analysis of Bias Compensated State Space Model Identification

閉ループ環境におけるバイアス補償型部分空間同定法の数値解析

第53回自動制御連合講演会論文集

第53回自動制御連合講演会論文集


Estimation of a Transition Time in Piecewise Linear Systems

区分的線形システムにおけるトランジションタイムの推定

第10回計測自動制御学会制御部門大会資料

第10回計測自動制御学会制御部門大会資料


Bias-Compensated State Space Identification Method

バイアス補償型部分空間同定法

第10回適応学習制御シンポジウム資料

第10回適応学習制御シンポジウム資料


Estimation of the transition time of piecewise linear systems by using wavelet analysis

ウェーブレット解析を用いた区分的線形システムのトランジションタイムの推定

第52回自動制御連合講演会講演論文集

第52回自動制御連合講演会講演論文集


Prediction of Stock Prices Change by Neural Network

ニューラルネットワークによる株価変動の予測

第52回自動制御連合講演会講演論文集

第52回自動制御連合講演会講演論文集


Consturuction and verification of stock prices forecast system using neural networks

ニューラルネットワークを用いた株売買支援システムの構築と検証

第52回自動制御連合講演会講演論文集

第52回自動制御連合講演会講演論文集


Application of Continuous Action Learning Automata in Global Optimal Solution Search of Unknown Function

未知関数の大域的最適解探索への連続動作学習オートマトンの適用

第52回自動制御連合講演会講演論文集

第52回自動制御連合講演会講演論文集


Construction of Autonomous Transfer System by Plural Movement Bodies Using Stochastic Real-Valued Unit

Stochastic Real-Valued ユニットを用いた複数移動体の自律移動システムの構築

第52回自動制御連合講演会講演論文集

第52回自動制御連合講演会講演論文集

The Institute of Systems, Control and Information Engineers


A parameter estimation method based on a reduced order plant model

対象の低次元モデルに基づくパラメータ推定法

第38回 制御理論シンポジウム資料

The Society of Instrument and Control Engineers


Change Prediction of Stock Prices by Using Neural Network

ニューラルネットワークを用いた株価の変動予測

Proceedings of the 53rd Annual Conference of the Institute of Systems, Control and Information Engineers

第53回システム制御情報学会研究発表講演会講演論文集


Intruder Capture System by Autonomous Transfer System Using Stochastic Real-Valued Unit

Stochastic Real-Valued ユニットを用いた自律移動体による侵入者捕獲システム

第53回システム制御情報学会研究発表講演会講演論文集


Global Optimal Solution Search of Unknown Function Using Continuous Action Learning Automata

連続動作学習オートマトンを用いた未知関数の大域的最適解探索

第53回システム制御情報学会研究発表講演会講演論文集


Bias-compensated least squares method in closed loop environment --- Identification under colored noise

閉ループ環境におけるバイアス補償型最小二乗法 --- 有色雑音下での同定

Proceedings of SICE 9th Annual Conference on Control Systems


Continuous-Time Model Estimation in Closed-Loop Environment --- On the relaxation of the identifiability condition

閉ループ環境における連続時間モデル推定 --- 可同定条件の緩和について

9th SICE Symposium on Adaptive and Learning Control

第9回適応学習制御シンポジウム資料


Bias Compensated Recursive Least Squares Estimate in Closed Loop Environment

閉ループ環境におけるバイアス補償型逐次最小二乗法

第51回自動制御連合講演会

第51回自動制御連合講演会


Autonomous Transfer System for Mobile Object Based on Stochastic Learning

確率的学習に基づく移動体自律移動システム

2008 SHIKOKU-SECTION JOINT CONVENTION RECORD OF THE INSTITUTES OF ELECTRICAL AND RELATED ENGINEERS

平成20年度電気関係学会四国支部連合大会講演論文集


Forecast System of Stock Prices by Nueral Networks

ニューラルネットワークによる株価予測システム

平成20年度電気関係学会四国支部連合大会講演論文集


Bias Compensated Recursive Least Squares Estimate in Closed Loop Environment --- On the improvement of the convergency

Bias Compensated Recursive Least Squares Estimate in Closed Loop Environment --- 収束性の改善について

第37回制御理論シンポジウム資料


Construction of pursuit-type variable hierarchical structure learning algorithm and its performance

追跡型可変階層構造学習アルゴリズムの構築とその性能

Proceedings of the 52nd Annual Conference of the Institute of Systems, Control and Information Engineers

第52回システム制御情報学会研究発表講演会講演論文集


Autonomous transfer system for mobile object using stochastic real-valued learning unit

確率的実数値出力学習ユニットを用いた移動体の自律移動システム

第52回システム制御情報学会研究発表講演会講演論文集

The Institute of Systems, Control and Information Engineers


Continuous-time model estimation in closed loop environment --- Iterative algorithm by using bias-compensated least squares method

閉ループ環境における連続時間モデル推定 --- バイアス補償型最小二乗法を用いた繰り返し型アルゴリズム

Proc. of the 52nd Annual Conference of ISCIE

第52回システム制御情報学会研究発表講演会講演論文集

Continuous-time model estimation in closed loop environment is proposed. The asymptotic bias of least squares estimate may be made zero by designing the prefilter appropriately. However in general, iterative re-design of the prefilter is required because it depends on the plant parameters. Bias-compensated least squares method is considered to improve the asymptotic bias of the LS estimate and it is considered to improve the convergence speed of the iteration. In this paper, a continuous-time model estimation method in closed loop environment by using bias-compensation is proposed.


Consturuction of stock prices forecast system by using neural networks

ニューラルネットワークを用いた株売買支援システムの構築

第52回システム制御情報学会研究発表講演会講演論文集


Bias-compensated Least Squares Method in Closed Loop Environment --- Fast iterative algorithm

閉ループ環境におけるバイアス補償型最小二乗法 --- 繰り返しアルゴリズムの高速化


Bias-Compensated Least Squares Method in Closed Loop Environment --- A filter design with colored noise

閉ループ環境におけるバイアス補償型最小二乗法 --- 有色雑音下でのフィルタ設計

In this paper, a bias-compensated least squares(BCLS) method in the closed loop environment is extended for the cases where the measurement noise is colored. Namely, a BCLS method in the closed loop environment based on Box-Jenkins model is proposed. Noise model is estimated based on a non-parametric mode or high order model because the purpose of the closed loop identification is th eimprovement of the plant dynamics and the purpose of the identification of the noise model is to obtain the plant parameter with smaller variances.


Bias-compensated Least Squares Method in Closed Loop Environment --- A formulation based on the transfer function representation

閉ループ環境におけるバイアス補償型最小二乗法 --- 伝達関数表現に基づく定式化

第50回自動制御連合講演会

第50回自動制御連合講演会


Bias-compensated Least Squares Method in Closed Loop Environment

閉ループ環境におけるバイアス補償型最小二乗法

第36回制御理論シンポジウム資料

第36回制御理論シンポジウム資料

The Society of Instrument and Control Engineers

In this paper, a bias-compensated least squares method in the closed loop environment is proposed. It is assumed that the observation noise is a white gaussian signal while there are no process noises. It is also assumed that the plant is controlled by a linear time invariant controller and that the closed loop system is asymptotically stable. It is shown that the asymptotic bias of the least squares estimate in the closed loop environment is represented as a difference between the parameter of the prefilter and that of an "optimal prefilter" and that the proposed bias-compensated least squares estimate is consistent.


Continuous-time model estimation by using adaptive observer --- Consistency of estimated parameters

適応観測器を用いた連続時間モデル推定 --- 推定パラメータの一致性について

Proceedings of the 51st Annual Conference of the Institute of Systems, Control and Information Engineers

第51回システム制御情報学会研究発表講演会講演論文集


Search for Global Optimal Solution of The Unknown Function by Using Variable Hierarchical Structure Learning Automata

ノイズを含む観測値に基づく学習オートマトンによる大域的最適解探


Autonomous Transfer System for Mobile Object Using Stochastic Real-Valued Reinforcement Learning Unit

実数値出力強化学習ユニットを用いた移動体の自律移動システム


Stock prices forecast system by using neural networks

ニューラルネットワークを活用した株価予測システム

The Institute of Systems, Control and Information Engineers


Continuous-time model estimation by using bias compensated adaptive observer

バイアス補償型適応観測器を用いた連続時間モデル推定

第7回適応学習制御シンポジウム資料

第7回適応学習制御シンポジウム資料


Continuous-time model estimation method by using adapitve observer --- Iterative design of the state variable filters

適応観測器を用いた連続時間モデル推定 --- 状態変数フィルタの繰り返し設計

第49回自動制御連合講演会資料

第49回自動制御連合講演会資料


The area weather forecast system based on improvement-type reinforcement learning unit

改良型強化学習ユニットを用いた地域気象予測システム

第49回自動制御連合講演会

第49回自動制御連合講演会


Continuous-time model identification by using adaptive observer --- Redesign of state variable filters

適応観測器を用いた連続時間モデル同定 --- 状態変数フィルタの再設計

Proc. of SICE 6th Annual Conference on Control Systems

This paper proposes a redesign method of the state variable filters(SVF's) which is used in the previously proposed continuous-time model estimation method by using adaptive observer. The SVF's are designed by using a Kalman filter which minimizes the variance of the residuals.


Parameter estimation of a continuous-time nonlinear system by using unscented Kalman filter

UKFを用いた非線形連続時間モデルのパラメータ推定

第50回システム制御情報学会研究発表講演会講演論文集

第50回システム制御情報学会研究発表講演会講演論文集

This paper proposes a continuous-time parameter estimation algorithm for nonlinear systems by using the Unscented Kalman Filter(UKF). The Ukf algorithm extends the Kalman Filter to nonlinear systems by transforming approximations of the probability distributions through the nonlinear process and measurement functions. Therefore, it is possible to calculate the estimate based on continuous-time model by using numerical integration, and make better estimation than the Extended Kalman Filter(EKF).


Continuous-time model identification by using adaptive observer --- Estimation of the noise model

適応観測器を用いた連続時間モデル同定 --- 雑音モデルの推定

Proc. of 6th SICE Symposium on Adaptive and Learning Control

第6回適応学習制御シンポジウム資料

The Society of Instrument and Control Engineers

This paper deasl with the estimation of the noise model in the previously proposed continuous-time model estimation method by using adaptive observer. Hybrid Box-Jenkins model is adopted for the noise model.


Stability analysis of continuous-time model identification method by using adaptive observer

適応観測器を用いた連続時間モデル同定の安定性解析

第48回自動制御連合講演会資料

第48回自動制御連合講演会資料

This paper proposes a continous-time model estimation method from sampled I/O data with the ZOH input assumption. State variable filters are modified in order to estimate the intersample signals by using a previously obtained parameter estimate. The bias of the estimated parameters is analyzed.

本論文は，零次ホールド入力の仮定のもと，入出力のサンプル値から連続時間モデルを推定する手法を提案している．状態変数フィルタは先に推定したパラメータに基づき出力のサンプル点間応答を推定する機構を含んでいる．さらに，状態変数フィルタを時不変な離散時間システムとして実現している．推定パラメータのバイアスについて解析し，推定誤差は観測雑音の大きさに比例することを示した．


Continuous-time model identification by using adaptive observer --- On the existence of an optimal solution

適応観測器を用いた連続時間モデル同定 --- 最適解の存在性について

Proc. of the 34th SICE Symposium on Control Theory

第34回制御理論シンポジウム資料

This paper proposes a continuous-time model estimation method from sampled I/O data with the ZOH input assumption. State variable filters are modified in order to estimate the intersample signals by using a previously obtained parameter estimate. A sufficient condition for the parameter convergence is analyzed.

サンプル値入出力データから連続時間モデルのパラメータを推定する手法を提案する．0次ホールド入力の仮定のもと，サンプル点間応答を推定するために，状態変数フィルタを修正する．推定パラメータが収束するための十分条件を与える．


Application of the variable hierarchical structure learning autmata to the search for global optimal solution of the unknown multimodal objective function

多峰性未知目的関数の大域的最適解探索への可変階層構造学習オートマトンの適用

平成17年度電気関係学会四国支部連合大会講演論文集

平成17年度電気関係学会四国支部連合大会講演論文集


The area weather forecast system using reinforcement learning unit

強化学習ユニットを用いた地域気象予測システムの構築

平成17年度電気関係学会四国支部連合大会講演論文集

平成17年度電気関係学会四国支部連合大会講演論文集


A design of state variable filters in continuous-time model identification

連続時間モデルの適応同定における状態変数フィルタの設計

Proc. of SICE 5th Annual Conference on Control Systems

The authors have proposed an identification method of continuous-time models by using adaptive observers. However, it was not clear how to construct the state variable filters, which will affect the performance of the identification. In this paper, several kinds of the realization of the state variable filters are compared by means of numerical simulations.


Regional rainfall forecast system based on reinforcement learning

強化学習に基づく地域降雨予測システム

第49回システム制御情報学会研究発表講演会講演論文集

第49回システム制御情報学会研究発表講演会講演論文集

The Institute of Systems, Control and Information Engineers


Continuous-Time Model Identification By Using Adaptive Observer --- Generalization of the State Variable Filters

適応観測器を用いた連続時間モデル同定 --- 状態変数フィルタの一般化

第47回自動制御連合講演会

第47回自動制御連合講演会


A recursive algorithm of continuous-time model identification for MIMO systems

多入出力系の繰り返し型連続時間モデル同定

第33回制御理論シンポジウム資料


Accuracy Improvement of Subspace Model Identification of Continuous Time System

部分空間法を用いた連続時間モデル同定の制度の改善に関する研究

平成16年電気関係学会四国支部連合大会講演論文集

平成16年電気関係学会四国支部連合大会講演論文集


Continuous-time model identification by using fast adaptive observer --- 収束性の改善

適応観測器を用いた連続時間モデル同定 --- 収束性の改善

This paper introduces a new method to identify a continuous-time model from sampled I/O data by using an adaptive observer based on RLS(Recursive Least Squares) method. The convergence of the parameter estimate is considereably improved compared to the steepest descent method. In order to identify the plant from a finite number of I/O data, an adaptive observer for a backward system is also proposed.


Search for Global Optimal Solution of The Unknown Function by Using Variable Hierarchical Structure Learning Automata

可変階層構造学習オートマトンによる未知関数の大域的最適解探索

Proceedings of the 48th Annual Conference of the Institute of Systems, Control and Information Engineers

第48回システム制御情報学会研究発表講演会講演論文集


The Area Weather Forecast System Using Reinforcement Learning Unit

強化学習ユニットによる地域気象予測システムの構築

第48回システム制御情報学会研究発表講演会講演論文集


適応観測器を用いた連続時間モデル同定 --- 不均一サンプリングの場合

適応観測器を用いた連続時間モデル同定 --- 不均一サンプリングの場合

第32回制御理論シンポジウム資料

第32回制御理論シンポジウム資料


動的テーブルによるWebアプリケーション自動合成

動的テーブルによるWebアプリケーション自動合成

第2回 情報科学技術フォーラム

第2回 情報科学技術フォーラム

ソフトウェアはプログラム言語を用いて記述されるが，記述されたプログラムコードを直感的に理解するのは容易ではない．このため，グラフィックスを用いた，さまざまのソフトウェア開発支援技術が研究されてきた．本論文では，Webアプリケーションの開発を例にとり，グラフィックスを用いることにより，ユーザが頭の中で描いたイメージに従ってソフトウェアを開発することができるイメージ指向プログラミング技法を提案し，データベースやプログラムテーブルの内容に基づいて，様々な種類のWebテーブルを動的に構成するWebアプリケーション自動合成方式について述べる．


サンプル値系の適応観測器

サンプル値系の適応観測器

第31回制御理論シンポジウム資料

第31回制御理論シンポジウム資料


Laminar stabilization control in chaos intermittency

間欠性カオスにおけるラミナー安定化制御

電子情報通信学会技術研究報告

Institute of Electronics, Information and Communication Engineers

Intermittency gives the irregular switching of almost periodic (laminar) and chaotic (burst) motions, and can be confirmed in every physical system within a certain parameter range. In this paper, by utilizing a method stabilizing a periodic motion which have been vanished by a saddle node bifurcation, a laminar motion in the intermittency is stabilized by a tiny control input. A pseudo periodic orbit composed by the vanished orbit or laminar motion is applied as a reference for the linear feedback control. We demonstrate the control for a circuit model and show robust stabilization responses.

間欠性カオスにおいては，ほぼ周期的な運動(ラミナー部) とカオス的な運動(バースト部) が不規則に切り 替えられている．間欠性カオスの存在するパラメータ付近に存在する，ラミナー部と同じ周期の周期解におけるサド ルノード分岐は，ラミナーの生成の直接的原因となり，かつ，ラミナー運動への初期値吸引能力を保存する仕組みと もなっている．そこで我々は，接線分岐前の周期解時系列を擬似周期軌道としてメモリに記録し，間欠性カオス状態 に，ラミナー運動と疑似周期軌道との誤差をフィードバックすることで，ごく少ない制御入力によりラミナー運動を 周期解へと安定化する．回路モデルに対してこの制御法を適用し，ロバストな制御結果を得た．


適応観測器を用いた連続時間モデル同定における収束条件について

適応観測器を用いた連続時間モデル同定における収束条件について

第4回適応学習制御シンポジウム資料

第4回適応学習制御シンポジウム資料

The Society of Instrument and Control Engineers


Web アプリケーションにおけるイメージ指向プログラミングの試み

Web アプリケーションにおけるイメージ指向プログラミングの試み

情報処理学会プログラミング研究会発表資料

情報処理学会プログラミング研究会発表資料

Webアプリケーションの開発において，開発者が頭の中で描いたイメージに従ってソフトウェアを開発することを可能とする，グラフィックスを用いたイメージ指向プログラミング技法を提案する．データベース，プログラムテーブル，Pivotテーブル等のビジュアルなWebコンポーネントと，Webページの遷移に基づいて実行されるビジネスロジックをドラッグ&ドロップによって結びつけることにより，Webアプリケーションを自動合成する方式について述べる．