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Parameter estimation for a multi-input multi-output state-space system with unmeasurable states through the data filtering technique

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IET CONTROL THEORY AND APPLICATIONS 2020年第19期14卷 3062-3075页

作者： Cui, Ting Ding, Feng Sheng, Jie Jiangnan Univ Sch Internet Things Engn Key Lab Adv Proc Control Light Ind Minist Educ Wuxi 214122 Jiangsu Peoples R China Univ Washington Sch Engn & Technol Tacoma WA USA

Parameter estimation is an important tool for modelling a real system. This study considers the parameter estimation problem of a multi-input multi-output state-space system with unmeasurable states. By employing the negative gradient search and cutting down redundant parameter estimates, the authors derive a partially-coupled generalised stochastic gradient (PC-GSG) algorithm to estimate the parameters. Considering the unmeasurable states, they present a new state observer which replaces the unknown parameters with their estimates to generate state estimates. By combining the PC-GSG algorithm and the new state observer, they obtain a state observer based partially-coupled generalised stochastic gradient (SO-PC-GSG) algorithm to estimate the parameters and states. In order to eliminate the interference of the coloured noise and strengthen the performance of the SO-PC-GSG algorithm, they propose a state observer based filtering PC-GSG algorithm by means of the data filtering technique. Finally, the effectiveness of the proposed algorithms is investigated in a simulation study.

关键词： control system synthesis observers stochastic systems filtering theory parameter estimation state-space methods gradient methods MIMO systems parameter estimation problem multiinput multioutput state-space system unmeasurable states redundant parameter estimates generalised stochastic gradient algorithm state observer unknown parameters SO-PC-GSG algorithm data filtering technique

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Parameter identification algorithm for ship manoeuvrability and wave peak model based multi-innovation stochastic gradient algorithm use data filtering technique

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DIGITAL SIGNAL PROCESSING 2024年 148卷

作者： Liu, Yang An, Shun Wang, Longjin He, Yan Fan, Zhimin Qingdao Univ Sci & Technol Coll Elect & Mech Engn Qingdao 266061 Peoples R China

This paper addresses the issue of identifying ship motion parameters and wave peak frequency. Utilising the Euler discretisation principle, we establish a discrete-time auto-regressive moving average model with exogenous input (ARMAX) for the ship-wave system. Furthermore, we develop a filtering-based stochastic gradient algorithm for the system by applying filtering techniques and auxiliary model identification idea. A filtering-based multiinnovation stochastic gradient algorithm, utilizing the multi-innovation identification theory, was developed to enhance the convergence rate and accuracy of parameter identification. This approach was found to be more effective than the filtering-based stochastic gradient algorithm. Simulation results validate the efficacy of the proposed algorithm in parameter identification.

关键词： Ship-wave discrete-time model Parameter model identification data filtering technique Multi-innovation identification theory Stochastic gradient algorithm

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Multi-innovation gradient estimation algorithms for multivariate equation-error autoregressive moving average systems based on the filtering technique

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IET CONTROL THEORY AND APPLICATIONS 2019年第13期13卷 2086-2094页

作者： Ma, Ping Ding, Feng Hayat, Tasawar Jiangnan Univ Key Lab Adv Proc Control Light Ind Minist Educ Sch Internet Things Engn Wuxi 214122 Peoples R China Qingdao Univ Sci & Technol Coll Automat & Elect Engn Qingdao 266061 Shandong Peoples R China King Abdulaziz Univ Dept Math Jeddah 21589 Saudi Arabia

This study concentrates on the parameter estimation of multivariate pseudo-linear autoregressive moving average systems by means of the multi-innovation identification theory and data filtering technique. A multi-innovation stochastic gradient algorithm is derived by introducing the innovation length in the stochastic gradient algorithm. Then, the original system is transformed into two subsystems by using a filter. A filtering-based multi-innovation stochastic gradient algorithm is presented, whose parameter estimation accuracy is higher than the multi-innovation stochastic gradient algorithm. The simulation results confirm that these two algorithms are effective.

关键词： gradient methods autoregressive moving average processes parameter estimation filtering theory multiinnovation gradient estimation algorithms multivariate equation-error autoregressive multivariate pseudolinear autoregressive moving average systems data filtering technique parameter estimation multiinnovation stochastic gradient algorithm

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Separation identification approach for the Hammerstein-Wiener nonlinear systems with process noise using correlation analysis

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL 2023年第14期33卷 8105-8123页

作者： Li, Feng Liang, Mingjun He, Naibao Cao, Qingfeng Jiangsu Univ Technol Coll Elect & Informat Engn Changzhou 213001 Peoples R China Yangzhou Univ Coll Elect Energy & Power Engn Yangzhou Peoples R China

This article develops a novel separation identification approach for the Hammerstein-Wiener nonlinear systems with process noise using correlation analysis technique. The Hammerstein-Wiener nonlinear systems have three parts, namely, an input nonlinear block, a linear block, and an output nonlinear block. The designed hybrid signals that consist of separable signal and random signal are devoted to achieving parameters separation identification of the Hammerstein-Wiener nonlinear system, that is, the three blocks are identified independently. First, the characteristics of separable signals under the action of static nonlinear block are analyzed, and two groups of separable signals with amplitude relation are utilized to estimate parameters of output nonlinear block. Moreover, the linear block parameters are identified by using correlation analysis approach, which deals with effectively immeasurable problem of internal variable information. Finally, the data filtering technique is implemented to weaken the influence of noises, and filtering-based recursive extended least squares algorithm is developed for figuring out the parameters of nonlinear block and colored noise model. The validity and accuracy of the proposed scheme are verified by two simulations, and simulation results exhibit that the proposed method can obtain higher identification precision and better robustness than the existing identification algorithms.

关键词： colored noise correlation analysis data filtering technique Hammerstein-wiener nonlinear systems separation identification

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A novel data filtering based multi-innovation stochastic gradient algorithm for Hammerstein nonlinear systems

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DIGITAL SIGNAL PROCESSING 2015年 46卷 215-225页

作者： Mao, Yawen Ding, Feng Jiangnan Univ Minist Educ Key Lab Adv Proc Control Light Ind Wuxi 214122 Peoples R China Jiangnan Univ Control Sci & Engn Res Ctr Wuxi 214122 Peoples R China

The identification of nonlinear systems is a hot topic in the identification fields. In this paper, a data filtering based multi-innovation stochastic gradient algorithm is derived for Hammerstein nonlinear controlled autoregressive moving average systems by adopting the key-term separation principle and the data filtering technique. The proposed algorithm provides a reference to improve the identification accuracy of the nonlinear systems with colored noise. The simulation results show that the new algorithm can more effectively estimate the parameters of the Hammerstein nonlinear systems than the multi-innovation stochastic gradient algorithm. (C) 2015 Elsevier Inc. All rights reserved.

关键词： Multi-innovation identification Nonlinear system Key-term separation principle data filtering technique

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filtering-based recursive least squares estimation approaches for multivariate equation-error systems by using the multiinnovation theory

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INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING 2021年第9期35卷 1898-1915页

作者： Ma, Ping Wang, Lei Jiangnan Univ Sch Artificial Intelligence & Comp Sci Jiangsu Key Lab Media Design & Software Technol Wuxi 214122 Jiangsu Peoples R China Nanjing Univ Informat Sci & Technol Binjiang Coll Wuxi Jiangsu Peoples R China

This article researches the filtering-based parameter estimation issues for a class of multivariate control systems with colored noise. A filtering-based recursive generalized extended least squares algorithm is derived, in which the data filtering technique is used for transforming the original system into two subidentification systems and the least squares principle is used for estimating parameters of these two subsystems. Furthermore, in order to improve the parameter estimation accuracy, the multiinnovation theory is added for deducing a filtering-based multiinnovation recursive generalized extended least squares algorithm. The numerical example confirms that these two proposed algorithms are effective.

关键词： data filtering technique least squares multiinnovation theory multivariate system parameter estimation

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data filtering-based parameter and state estimation algorithms for state-space systems disturbed by coloured noises

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INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE 2020年第9期51卷 1669-1684页

作者： Cui, Ting Ding, Feng Alsaedi, Ahmed Hayat, Tasawar Jiangnan Univ Sch Internet Things Engn Minist Educ Key Lab Adv Proc Control Light Ind Wuxi 214122 Jiangsu Peoples R China Qingdao Univ Sci & Technol Coll Automat & Elect Engn Qingdao 266061 Peoples R China King Abdulaziz Univ Dept Math Jeddah Saudi Arabia

In this paper, the combined parameter and state estimation issues of state-space systems are considered, and the process noises and observation noises are supposed to be coloured noises. By utilising the data filtering technique, we transform the original state-space system into the filtered system for eliminating the interference of the coloured noise in the state equation, and then we derive a filtering-based extended stochastic gradient (F-ESG) algorithm to estimate the system parameters. For estimating the unmeasurable states, we derive a new state estimator by using the preceding parameter estimates to take the place of the unknown system parameters in the Kalman filter. Furthermore, we propose a filtering-based multi-innovation extended stochastic gradient (F-MI-ESG) algorithm to achieve the higher parameter estimation accuracy. Finally, we provide two simulation examples to test and compare the performance of the proposed algorithms. The simulation results indicate that the F-ESG algorithm and the F-MI-ESG algorithm are effective for parameter estimation, and that the F-MI-ESG algorithm is able to achieve more accurate parameter estimates than the F-ESG algorithm.

关键词： State-space system parameter estimation data filtering technique gradient search multi-innovation identification

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Iterative parameter identification for pseudo-linear systems with ARMA noise using the filtering technique

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IET CONTROL THEORY AND APPLICATIONS 2018年第7期12卷 892-899页

作者： Ding, Feng Xu, Ling Alsaadi, Fuad E. Hayat, Tasawar Jiangnan Univ Sch Internet Things Engn Lab Adv Proc Control Light Ind Minist Educ Wuxi 214122 Peoples R China Qingdao Univ Sci & Technol Coll Automat & Elect Engn Qingdao 266042 Peoples R China King Abdulaziz Univ Dept Elect & Comp Engn Fac Engn Jeddah 21589 Saudi Arabia

This study considers the parameter identification problem of a pseudo-linear autoregressive moving average system (i.e. linear-in-parameter autoregressive output-error ARMA systems), whose disturbance is an ARMA process. By means of the filtering technique, a filtering-based gradient iterative (F-GI) algorithm and a filtering-based least squares iterative (LSI) algorithms are presented for enhancing the estimation accuracy. Furthermore, a filtering-based decomposition LSI algorithm is derived for improving the computational efficiency. The key is to use the hierarchical identification principle, to apply the data filtering technique for identification, and to replace the unknown terms in the information vectors with their estimates. Compared with the F-GI algorithm, the filtering-based LSI algorithm and the filtering-based decomposition LSI algorithm have faster convergence rates. The simulation results indicate that the proposed algorithms are effective.

关键词： linear systems autoregressive moving average processes filtering theory parameter estimation iterative methods gradient methods least squares approximations iterative parameter identification ARMA noise filtering technique pseudolinear autoregressive moving average system linear-in-parameter autoregressive output-error ARMA systems filtering-based gradient iterative algorithm F-GI algorithm filtering-based least squares iterative algorithm LSI algorithms hierarchical identification principle data filtering technique information vectors filtering-based decomposition LSI algorithm convergence rates

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filtering-based multi-innovation recursive identification methods for input nonlinear systems with piecewise-linear nonlinearity based on the optimization criterion

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OPTIMAL CONTROL APPLICATIONS & METHODS 2022年第3期43卷 884-903页

作者： Fan, Yamin Liu, Ximei Qingdao Univ Sci & Technol Coll Automat & Elect Engn Qingdao 266061 Peoples R China

Piecewise-linear nonlinearity is an effective representation for hard nonlinearities such as saturation, dead zone, and backlash, which can be also used as a general tool for approximating nonlinear characteristics. For the input piecewise-linear output-error autoregressive systems, we develop a parametric expression of the piecewise-linear nonlinearity through a switching function and position functions, and derive the identification model of the system by using the key item separation. Based on the optimization criterion, an auxiliary model-based multi-innovation recursive generalized least-squares algorithm is deduced for estimating the unknown parameters according to the obtained model. Since the system is disturbed by colored noise, we introduce the data filtering technique from a view point of improving the parameter estimation accuracy. The filtering identification model of the system is derived and a filtering-based multi-innovation recursive generalized least-squares algorithm is proposed. The simulation example demonstrates the effectiveness of the proposed algorithms and shows that the filtering-based multi-innovation recursive generalized least-squares algorithm has better identification performance.

关键词： data filtering technique key item separation multi-innovation theory optimization criterion parameter estimation piecewise-linear nonlinearity

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data filtering-Based Multi-innovation Stochastic Gradient Algorithm for Nonlinear Output Error Autoregressive Systems

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CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2016年第2期35卷 651-667页

作者： Mao, Yawen Ding, Feng Jiangnan Univ Minist Educ Key Lab Adv Proc Control Light Ind Wuxi 214122 Peoples R China

This paper discusses the parameter estimation problems of nonlinear output error autoregressive systems and presents a data filtering-based multi-innovation stochastic gradient algorithm for improving the parameter estimation accuracy of the stochastic gradient algorithm by combining the multi-innovation identification theory and the data filtering technique. The proposed algorithm is effective and can generate highly accurate parameter estimates compared with the multi-innovation stochastic gradient algorithm. The simulation results confirm this conclusion.

关键词： Stochastic gradient Multi-innovation identification theory data filtering technique Nonlinear system

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