Online identification of non-linear dynamic systems by Wiener model using subspace method and neural networks

Sadeghi, Meysam; Farrokhi, Mohammad

doi:10.1177/0142331216663618

Cited by 10 publications

(3 citation statements)

References 33 publications

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“…Because of good parallel structure and better learning ability, neural networks have better approximately capability for the modeling, identification and control of nonlinear systems (Li et al, 2015; Lin et al, 2018; Montague et al, 1996; Sadehgi and Farrokhi, 2018). The training procedure with large number of iterations was the cause of slower convergence and higher computational complexity.…”

Section: Introductionmentioning

confidence: 99%

Retracted: Adaptive nonlinear backstepping control using mended recurrent Romanovski polynomials neural network and mended particle swarm optimization for switched reluctance motor drive system

Lin

2019

Transactions of the Institute of Measurement and Control

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A switched reluctance motor (SRM) drive system has highly nonlinear uncertainties owing to a convex construction. It is hard for the linear control methods to achieve good performance for the SRM drive system. An adaptive nonlinear backstepping control system using the mended recurrent Romanovski polynomials neural network and mended PSO with an adaptive law and an error estimated law is proposed to estimate the lumped uncertainty and to compensate the estimated error in order to enhance the robustness of the SRM drive system. Additionally, in accordance with the Lyapunov stability theorem, the adaptive law in the mended recurrent Romanovski polynomials neural network and the error estimated law are established. Furthermore, to help improve convergence and to obtain better learning performance, the mended particle swarm optimization (PSO) algorithm is utilized for adjusting the two varied learning rates of the two parameters in the mended recurrent Romanovski polynomials neural network. Finally, some experimental results and a comparative analysis are verified that the proposed control scheme has better control performances for controlling the SRM drive system.

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Section: Introductionmentioning

confidence: 99%

Retracted: Adaptive nonlinear backstepping control using mended recurrent Romanovski polynomials neural network and mended particle swarm optimization for switched reluctance motor drive system

Lin

2019

Transactions of the Institute of Measurement and Control

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“…8,9 The Wiener models, which are composed of a linear filter and a static nonlinearity, are a class of output nonlinear systems, and they have been proved to be useful as nonlinear models for many practical applications, such as continuous stirred tank reactors, 10,11 solid oxide fuel cell, 12 pH neutralization process, 13 wind power forecasting, 14 and so on. Over the past few decades, a lot of effective approaches have been developed for dealing with the problem of the Wiener systems identification, including over parameterization algorithm, 15 subspace method, 16,17 frequency-type method, 18 exciting signals-based techniques, 19,20 iterative algorithm, [21][22][23] auxiliary model-based multi-innovation method, [24][25][26] and least squares method. 27 Since noises widely exist in actual industrial processes, and are often colored noise, which play a vital influence on system identification, thus it is very significant to focus on the Wiener systems with noises.…”

Section: Introductionmentioning

confidence: 99%

Estimation of wiener nonlinear systems with measurement noises utilizing correlation analysis and Kalman filter

Li,

Qian,

et al. 2024

Intl J Robust & Nonlinear

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This paper is concerned with parameter estimation of Wiener systems with measurement noises employing correlation analysis method and adaptive Kalman filter. The presented Wiener system consists of two series blocks, that is, a dynamic block represented by auto‐regressive moving average (ARMA) model, and static nonlinear block established by neural fuzzy model. Aim at estimating separately the two blocks, the separable signals are introduced. First, applying the separable signals to decouple the identification of linear dynamic block from that of static nonlinear block, then ARMA model parameters are estimated employing correlation function‐based least squares principle. Moreover, aiming at handle with error caused by colored measurement noise, adaptive Kalman filter technique and cluster method are introduced to estimate parameter of the nonlinear block and noises model, enhancing parameter estimation precision. The accuracy and applicability of estimated scheme presented are verified through numerical simulation and nonlinear process, the results demonstrate that it is feasible for estimating the Wiener systems in the presence of colored measurement noises.

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“…Because of good parallel structure and better learning ability, neural networks have better approximately capability for the modeling, identification and control of nonlinear systems (Lin, 2015, 2019; Lin et al, 2002, 2018; Sadehgi and Farrokhi, 2018). The training procedure with large number of iterations was the cause of slower convergence and higher computational complexity.…”

Section: Introductionmentioning

confidence: 99%

Smart backstepping control using revised recurrent fuzzy neural network and revised ant colony optimization for linear permanent magnet synchronous motor drive system

Lin

Chang

2019

Transactions of the Institute of Measurement and Control

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Because of the uncertainty’s action in a linear permanent magnet synchronous motor drive system such as the external load force, the cogging force, the column friction force and the Stribeck effect force and the parameters variations, it is difficult to reach specific control performances by using the existing linear controller. To raise robustness under occurrence of parameters uncertainties and external force disturbances, the smart backstepping control system with three adaptive laws is proposed for controlling the linear permanent magnet synchronous motor drive system. In accordance with the Lyapunov function, three adaptive laws are derived to ameliorate the system’s robustness. Furthermore, the smart backstepping control system using revised recurrent fuzzy neural network and revised ant colony optimization with the compensated controller is proposed to improve the control performance. The revised recurrent fuzzy neural network acts as the estimator of the uncertainty’s disturbances. In addition, the compensated controller with error estimation law is proposed to compensate the minimum rebuilt error. Moreover, two learning rates of the weights in the revised recurrent fuzzy neural network are derived according to the discrete-type Lyapunov stability to assure convergence of the output tracking error and are adopted by using the revised ant colony optimization to speed-up parameter’s convergence. Finally, some comparative performances are verified through some tentative upshots that the smart backstepping control system by virtue of revised recurrent fuzzy neural network and revised ant colony optimization with the compensated controller results in better control performances for the linear permanent magnet synchronous motor drive system.

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Online identification of non-linear dynamic systems by Wiener model using subspace method and neural networks

Cited by 10 publications

References 33 publications

Retracted: Adaptive nonlinear backstepping control using mended recurrent Romanovski polynomials neural network and mended particle swarm optimization for switched reluctance motor drive system

Retracted: Adaptive nonlinear backstepping control using mended recurrent Romanovski polynomials neural network and mended particle swarm optimization for switched reluctance motor drive system

Estimation of wiener nonlinear systems with measurement noises utilizing correlation analysis and Kalman filter

Smart backstepping control using revised recurrent fuzzy neural network and revised ant colony optimization for linear permanent magnet synchronous motor drive system

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