Shunshoku Kanae

• Fukui University of Technology

This paper investigates the problem of identifying errors-in-variables (EIV) models, where the both input and output measurements are corrupted by white noises, and addresses a new efficient recursive algorithm. The identification problem of EIV models with unknown noise variances has been extensively studied and several methods have been proposed....

This paper investigates the problem of identifying errors-in-variables (EIV) models, where the both input and output measurements are corrupted by white noise, and addresses a new method to solve the problem. The identification problem of EIV models with unknown noise variances has been extensively studied and several methods have been proposed. To...

Artificial respirators are widely used in various scenes. Doctors are required to pay scrupulous attention for the use of the artificial respirators. The ideal setting method of artificial respirator is a setting method in consideration of the pulmonary characteristic of the patient. However, we cannot know the pulmonary characteristic of the patie...

In artificial respiration, setting of ventilation conditions is expected to fit each patient. Characteristics of people's respiratory systems are very different and those values are very difficult to be measured directly. The aim of our research is to establish a framework to modeling the respiratory system and to set appropriate ventilation condit...

The ventilation condition of artificial respiration should be set carefully, and the setting is expected to match individual pulmonary characteristic of each patient. To this purpose, the pulmonary elastance estimation method based on fuzzy logic has been proposed by the authors. In this paper, a recursive estimation method of estimating respirator...

Artificial respirators are widely used for patients with little or no autonomous breathing ability. Doctors are required to pay scrupulous attention for the use of the artificial respirators. And doctors must set the artificial respirator in consideration of each patient's pulmonary characteristic. However, we do not understand the pulmonary charac...

Artificial respirators are widely used for patients with little or no autonomous breathing ability. Doctors are required to pay scrupulous attention for the use of the artificial respirators. And doctors must set the artificial respirator in consideration of each patient’s pulmonary characteristic. However,the setting of the artificial respirator i...

Establishing a respiratory model and estimating pulmonary elastance of patient is the bases for suitable setting of ventilation conditions. In our recent work, the nonlinear differential equation model of respiration with fuzzy elastance is addressed. In this paper, an ant colony optimization algorithm is proposed to adjust parameters of fuzzy memb...

Pulmonary characteristics differ in patients, and the suitable setting of ventilation condition is needed for every patient in the artificial respiration. The pulmonary elastance is one of the important features of lung, and it is a basis for deciding the airway pressure limit value. To get the pulmonary elastance of the of the patient from measure...

Parameter adaptation and disturbance observer (DOB) have been considered as two contrastively different approaches in motion control problems. In the authors' previous work, the two techniques were merged into one control design with a theoretically guaranteed performance. A drawback of the method is that the controller has to be relatively complic...

This paper considers the output-tracking control problem of feedback linearizable nonlinear systems in the presence of external disturbances and modeling errors. A robust output feedback nonlinear controller is designed to achieve excellent output-tracking performance. By exploiting the cascade features of backstepping design, a simple disturbance...

This technical note presents a unified framework for bias compensation principle (BCP)-based methods applied for identification of linear systems subject to correlated noise. By introducing a non-singular matrix and an auxiliary vector uncorrelated with the noise, the unified framework is established. Since there are rich possibilities of the choic...

Pulmonary characteristics differ in patients, and the suitable setting of ventilation condition is needed for every patient in the artificial respiration. The pulmonary elastance is one of the important features of lung, and it is a basis for deciding the air-way pressure limit value. In this study, an estimation method for pulmonary elastance is p...

Building a precise respiratory model is very helpful for setting appropriate ventilation conditions to fie each patient when artificial respiration is performed on the patient. The authors have proposed two types of second order nonlinear differential equation model of respiratory system. However, these model cannot cover the hysteresis characteris...

To perform artificial respiration safely and comfortably, it is necessary to get the information on characteristics of patient respiratory system timely, and to set the ventilation conditions fitted to each patient under the information. Based on our earlier works on modeling and estimation of respiratory system, in this paper, data averaging metho...

Parameter adaptation and disturbance observer (DOB) have been considered as two contrastively different approaches to handle uncertainties in motion control problems. The purpose of this brief is to merge both techniques into one control design with theoretically guaranteed performance. It is shown that the DOB compensates low-passed components of...

This paper proposes a novel robust output feedback controller for an electromechanical system in the presence of external disturbance and uncertainties of physical parameters. By exploiting the cascade features of backstepping design, a simple disturbance observer is proposed to suppress the effects of the uncertainties, and a high-gain observer is...

The ability to identify and follow a moving object is not only important for human activities, but it is also critical necessity in the use of robots for automation and manufacturing, security applications and for life sciences. This can be achieved ...

In this paper, the method of consistent estimation of the errors-in-variables (EIV) models based on the quantized input-output measurements is studied. A new bias-compensation based method, named the bias-compensated instrumental variable type (BCIV-type) method, has been proposed for the quantized EIV models identification. The proposed BCIV-type...

Building a precise respiratory model is very helpful for setting appropriate ventilation conditions to fit each patient when artificial respiration is performed on the patient. The authors have proposed two types of second order nonlinear differential equation model of respiratory system. However, these model cannot cover the hysteresis characteris...

A robust non-linear output-feedback controller by the K-filter approach is proposed for position-tracking problem of a magnetic levitation system, where only the position measurement is available for control. Instead of the popular adaptive control techniques, a disturbance observer (DOB) is merged into the K-filter-based output-feedback controller...

This paper addresses identification of Hammerstein systems using the sampled input-output data in frequency domain. When the static memoryless nonlinear part of Hammerstein model can be considered as a polynomial with a known order, the following linear part can be estimated as a multi-input single-output (MISO) systems with the numerical algorithm...

This paper deals with the problem of estimating the parameters of Hammerstein systems based on recursive least squares method. Hammerstein systems can be considered as one kind of nonlinear systems, it is applied in many fields. Several identification methods of Hammerstein systems have been developed. We utilize the recursive algorithm to identify...

It is well known that least-squares (LS) method gives biased parameter estimates when the input and output measurements are corrupted by white noises. One possible approach for solving this bias problem is the bias-compensation based method such as the bias-compensated least-squares (BCLS) method. In this paper, a new bias-compensation based method...

This paper proposes a new method of decentralized robust control for large-scale interconnected uncertain nonlinear mechanical systems, by using disturbance observers (DOBs). Rigorous stability analysis is given for the overall nonlinear system. Simulation results on a coupled double pendulum system are presented to confirm the established theoreti...

In this paper, decision of air-pressure limit value in the case of artificial respiration is considered. Air-pressure limit value is an important conditional parameter of artificial respiration. the pulmonary characteristics are very different according to the person. For setting appropriate ventilation conditions fitting to each patient, it is nec...

In many cases it is not possible to remove the feedback during an identification experiment because this may lead to instability. The feedback may also be inherent in many dynamic systems (e.g., economical, biological). In this paper we shall consider the problem of identification of dynamic systems operating under feedback control and propose an i...

We have proposed an interpretation of MOESP types of subspace algorithms by using the Schur complement (SC) of the data product moment and proposed a unified framework for the subspace-based identification. Here we consider the introduction of exponential forgetting factor which windows the data matrices to apply the algorithms to the slowly time-v...

It is well known that the least-squares (LS) method gives biased parameter estimates of transfer functions when the output measurement is corrupted by white noise. One possible approach for solving this bias problem is the total-least-squares (TLS) method. Davila proposed a Recursive TLS (RTLS) algorithm based on minimization of the generalized Ray...

It is well known that least-squares (LS) method gives biased parameter estimates when the input and output measurements are corrupted by white noise. One possible approach for solving this bias problem is the bias-compnesation based method such as the bias-compensated least-squares (BCLS) method. In this paper, a new bias-compensation based method...

In this paper, we consider the identification problem for a dual-rate system in which the input sampling period may differ from that of the output. Based on the lifting operators, a lifted system which is equivalent to the original dual-rate system can be derived so that a lifted state-space model can be obtained which maps the relations between th...

This paper proposes an adaptive robust output-feedback controller for the position-tracking problem of a magnetic levitation system with a current-feedback power amplifier. The system is governed by a single-input single-output second-order nonlinear differential equation which is different from the standard output-feedback form, since there is a p...

In this brief, a novel robust nonlinear motion controller with disturbance observer (DOB) for positioning control of a nonlinear single-input-single-output (SISO) mechanical system is proposed. The controller is designed in a backstepping manner. First, a proportional-integral (PI) controller is designed to stabilize the position error. Consequentl...

The MOESP types of the subspace algorithms which are originally proposed by Verhaegen are considered at the point of view from the weighting of the data matrices. We have proposed an interpretation of these types of subspace algorithms by using the Schur complement (SC) of the data product moment and derive a unified framework for the subspace-base...

It is well known that least-squares (LS) method gives biased parameter estimates when the input and output measurements are corrupted by noise. One possible approach for solving this bias problem is the bias-compensation based method such as the bias-compensated least-squares (BCLS) method. In this paper, a new bias-compnesation based method is pro...

This paper considers the control problem of a popular magnetic levitation system, which is open-loop unstable and strongly nonlinear associated with the electromechanical dynamics. The system dynamics is governed by a third-order nonlinear differential equation. The overall controller is designed through a backstepping manner by combining both the...

This article considers the adaptive robust control of a class of single-input-single-output nonlinear systems in semi-strict feedback form using radial basis function (RBF) networks. It is well known that the standard backstepping design may suffer from ''explosion of terms''. To overcome this problem, the recently developed dynamic surface control...

This paper considers the identification problem of multiple input single output (MISO) continuous-time systems with unknown time delays of the inputs, from sampled input–output data. An iterative global separable nonlinear least-squares (GSEPNLS) method which estimates the time delays and transfer function parameters separably is derived to signifi...

Air-pressure limit value is an important conditional parameter of artificial respiration. The pulmonary characteristics are very different according to the person. For setting appropriate ventilation conditions fitting to each patient, it is necessary to establish a mathematical model describing the mechanism of human respiratory system, and to kno...

In this paper, a practical adaptive robust nonlinear controller is proposed for motion control of an SISO nonlinear mechanical system, where the distrubances due to ripple force and friction are compensated by the RBF networks. Rigorous analysis of transient performance and ultimate bound is given. Numerical examples are included to verify the theo...

In this paper, we consider the identification problem for a dual-rate system in which the input sampling period may differ from that of the output. Based on the lifting oper-ators, a lifted system which is equivalent to the original dual-rate system can be derived so that a lifted state-space model can be obtained which maps the relations between t...

Pulmonary elastance provides an important basis for deciding air pressure parameters of mechanical ventilators, and airway resistance is an important parameter in the diagnosis of respiratory diseases. The authors have proposed a second order nonlinear differential equation model of respiratory system whose elastic and resistant coefficients are ex...

It is well-known that the Least Squares (LS) method gives biased estimates for Infinite Impulse Response (IIR) model in the presence of output noise. One of the methods which delivers consistent estimates is the Total Least Squares (TLS) method. In this paper, two types of the recursive TLS algorithms for noisy IIR estimation are discussed.

Recursive updating algorithms of error covariance matrices in subspace identification methods for time-varying systems are derived. The proposed algorithms can be applied to estimate the system parameters which are slowly time-varying. The algorithms are based on the fact that the subspace extraction amounts to computing singular value decompositio...

This paper presents two novel radial basis functions and their comparison. Both radial basis functions are based on an idea of Support Vector Machine (SVM) by mapping data into a high dimensional feature space, which is known as Reproducing Kernel Hilbert Space and then performing Radial Basis Function (RBF) network in the feature space. Orthogonal...

Pulmonary elastance provides an important basis for deciding air pressure parameters of mechanical ventilators, and airway resistance is an important parameter in the diagnosis of respiratory diseases. The authors have proposed two types of second order nonlinear differential equation model of respiratory system. In the first type of model, elastic...

Using a unified approach, recursive algorithms of the error covariance matrices in subspace methods are derived for the MOESP type of subspace methods. The proposed approach is based on the fact that the subspace extraction amounts to computing singular value decomposition of the Schur complement (SC) of the input submatrix in data product moments...

In this paper, a practical and general adaptive robust nonlinear controller is proposed for positioning control of a nonlinear mechanical system. To overcome the main obstacles that prevent the adaptive control techniques from coming into wide use in the industrial side, our attention is focused on the guaranteed transient performance and theoretic...

Using a unified approach, recursive subspace identification algorithms are derived for the MOESP type of subspace methods. The proposed approach is based on the fact that the subspace extraction amounts to computing singular value decomposition of the Schur complement (SC) of the input submatrix in data product moments and the SC can be interpreted...

In this paper, two bias-compensated least-squares (BCLS) methods (BCLS-α method and BCLS-β method) are proposed for identification of linear discrete-time system in the case where the input measurement is corrupted by white noise and the output measurement is corrupted by colored noise. It is well known that BCLS method is based on compensation of...

The present study employs an idea of mapping data into a high dimensional feature space which is known as Reproducing Kernel Hilbert Space (RKHS), then performs Radial Basis Function (RBF) network in the feature space where the new basis function will be obtained and finally, Orthogonal Least Squares (OLS) method is employed to select a suitable se...

The Structured Total Least Squares (STLS) problem is an extension of the Total Least Squares (TLS) problem for solving an overdetermined system of equations Ax ≈ b 1). The Dynamic Total Least Squares (DTLS) problem is a special case of STLS problem. In this paper, we illustrate the TLS singular value problem by defining some vectors from a new view...

This paper considers the identification problem of continuous-time systems with unknown time delays from sampled input-output data. An iterative global separable nonlinear least-squares (GSEPNLS) method which estimates the time delays and transfer function parameters separably is derived, by using stochastic global-optimization technique to avoid c...

This paper considers the position tracking problem of a popular magnetic levitation system in the presence of modeling errors due to uncertainties of physical parameters. The recently developed dynamic surface control (DSC) technique is modified and applied to the system under study, to overcome the problem of “explosion of terms” associated with t...

For setting appropriate respiratory conditions to fit each patient who is receiving artificial respiration treatment, it is very important to build a respiratory model of lung that describes the dynamics of respiration. In this paper, a new respiratory model is proposed, which is a second order nonlinear differential equation with elastic and resis...

Building a precise respiration system model is very helpful for setting appropriate ventilation conditions to fit each patient when artificial respiration is performed on the patient. In this paper, a new respiration system model is proposed, which is a second order nonlinear differential equation including volume dependent elastic term described b...

This paper considers the identification problem of continuous-time systems with unknown time delays from sampled input-output data. By using a digital prefilter, an approximated discrete-time estimation model is first derived, in which the system parameters remain in their original form and the time delays need not be an integral multiple of sampli...

The present study employs an idea of mapping data into a high dimensional feature space which is known as Reproducing Kernel Hilbert Space (RKHS), then performs Radial Basis Function (RBF) network in the feature space and finally, Orthogonal Least Squares (OLS) method is employed to select a suitable set of centers (regressors) from a large set of...

In this paper, we show the experimental study on adaptive robust neural network Principal Compo- nent Analysis (PCA) based on a reconstruction er- ror model. Firstly we explain the traditional batch way PCA which is based on eigenvalue decompo- sition and discuss its problems. To overcome such the problems, the adaptive robust neural network Princi...

In this paper, a unified approach for subspace-based identification methods by using Schur complement is proposed. MOESP (MIMO output error state space model identification) type of subspace idenitification methods are well known as an elementary subspace method. The extensions of the MOESP with instrumental variables (IV) have been proposed, which...

In this paper a study is presented on Subspace State Space Identification (4SID) method for the SISO (Single-Input Single-Output) systems. It is seen that an intermediate vector which appears in the 4SID procedure is equal to the parameter of system pulse transfer function. It is also shown that 4SID method is equivalent to Eigenvector (EV) method...

This paper considers the position-tracking problem of a magnetic levitation system in the presence of modeling errors due to uncertainties of physical parameters. A robust nonlinear controller is designed to achieve excellent position-tracking performance. The recently developed dynamic surface control is modified and applied to the system under st...

This paper considers the position tracking problem of a magnetic levitation system in the presence of uncertainties of physical parameters. The dynamic surface control (DSC) technique is modified and applied to the system under study, to overcome the problem of "explosion of terms" associated with the backstepping design procedure. Input-to-state s...

This paper focuses on bias compensation estimation of autoregressive (AR) process in the presence of white noise. It is known that bias compensation principle (BCP) based method requires the estimate of unknown noise variance to compensate the bias of least-squares (LS) estimate to provide consistent AR parameter estimate. In this paper, estimation...

This paper considers the identification problem of continuous-time systems with unknown time delay from sampled input-output data. By using a digital pre-filter, an approximated discrete-time estimation model is first derived, in which the system parameters remain in their original form and the time delay need not be an integral multiple of the sam...

This paper considers the identification problem of continuous-time systems with unknown time delay from sampled input-output data. By using a digital pre- filter, an approximated discrete-time estimation model is first derived, in which the system parameters remain in their original form and the time delay need not be an integral multiple of the sa...

This paper shows a new interpretation of the subspace-based identifica- tion methods by using Schur complement approach. MOESP (MIMO output error state space model identification) algorithms are considered. Instead of the data matrices, we start to consider a data product moment consisted of the Hankel matrices of input-output data. It is shown tha...

This paper studies the problem of parameter estimation of ARMAX model from a novel point of view. An efficient bias compensation least squares algorithm is proposed to provide consistent parameter estimate for ARMAX model. The main feature of our proposed algorithm is to introduce the auxiliary least squares linear backward predictors to construct...

In many cases it is not possible to remove the feedback during an identification experiment because this may lead to instability. The feedback may also be inherent in many dynamic systems (e.g., economical, biological). In this paper we shall consider the problem of identification of dynamic systems operating under feedback control and propose an i...

In this study, we proposed Kernel Principal Component Analysis (KPCA) which is applied for feature selection in a high-dimensional feature space which is nonlinearly mapped from an input space by a Gaussian kernel function. By using Mercer Kernels, we can compute principal components in a high dimensional feature space. Then, the extracted features...

In this paper, an interpretation of subspace-based identification methods with Schur complement approach is proposed. MOESP (MIMO output error state space model identification) algorithms are well known as an elementary subspace method. The extensions of the MOESP with instrumental variables (IV) have been proposed in literatures, which can be usef...

In this paper, we propose an efficient procedure of physical parameter identification of a magnetic levitation system, where the levitated steel ball is controlled by a robust nonlinear controller which is designed based on rough nominal parameters. Design techniques of the robust nonlinear controller are described and parameter identification resu...

In this study, Kernel Principal Component Analysis (KPCA) is applied as feature selection in a high-dimensional feature space which is nonlinearly related to an input space. By using Mercer Kernels, we can compute principal components in a high dimensional feature space. Then, the extracted features by KPCA method are employed as a new kind of regr...

This paper considers the position tracking problem of a popular magnetic levitation system in the presence of modeling errors due to uncertainties of physical parameters. The recently developed dynamic surface control (DSC) technique is modified and applied to the system under study, to overcome the problem of ``explosion of terms'' associated with...

A novel robust nonlinear motion controller with disturbance observer (DaB) for positioning control of a nonlinear SISa mechanical system is proposed. By using some elegant nonlinear damping terms, the input-to-state stability (ISS) property of the overall nonlinear control system is proved, which leads to a major contribution of construction of a t...

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