Intelligent Sliding-Mode Position Control Using Recurrent Wavelet Fuzzy Neural Network for Electrical Power Steering System

Lin, Faa-Jeng; Chen, Shih-Gang; Sun, I-Fan

doi:10.1007/s40815-017-0342-x

Cited by 27 publications

(10 citation statements)

References 30 publications

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“…Also, the weighting vector can be the adjustable parameters of the fuzzy system in the consequent part of the fuzzy rules, Legendre coefficients or the Fourier series coefficients. It follows from (34) and (36) thatUsing (37) and (38), we can write As a result, (40) can be rewritten as The sampling interval in the experiment is short enough as compared with the variation of m , thus, the term m is assumed to be a constant during the estimation (i.e.̃m = m −̂m →̇̃m = −̇̂m ) [39][40][41].…”

Section: The Proposed Controllermentioning

confidence: 99%

Design of an adaptive dynamic sliding mode control approach for robotic systems via uncertainty estimators with exponential convergence rate

2020

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In this work, an adaptive dynamic sliding mode control approach is proposed for robotic systems via uncertainty estimators with exponential convergence rate. The uncertainties are estimated using various uncertainty estimators such as the Fourier series expansion, Legendre polynomials and adaptive fuzzy systems. Also, for each uncertainty estimator, the approximation error is compensated. The adaptation laws are derived using a stability analysis. Moreover, the asymptotic convergence of the tracking error and the boundedness of all closed-loop signals are guaranteed. The novelty of this paper is proposing a positive exponential function for the convergence rate of the adaptation rules to prevent from initial high voltages originated from large initial tracking errors. Another novelty of this paper is presenting a robust control term for the truncation error that improves the accuracy of the control system. Analysis of simulations reveals the effectiveness of the proposed method in terms of fast disturbance rejection and negligible tracking error.

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Section: The Proposed Controllermentioning

confidence: 99%

Design of an adaptive dynamic sliding mode control approach for robotic systems via uncertainty estimators with exponential convergence rate

2020

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“…Since the sampling interval in the experiment is short enough as compared with the variation of h, the uncertain term h is also assumed to be a constant during the estimation (Lin et al, 2007, 2017).…”

Section: The Proposed Control Lawmentioning

confidence: 99%

Robust dynamic sliding mode control of robot manipulators using the Fourier series expansion

Shokoohinia

Fateh

2018

Transactions of the Institute of Measurement and Control

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This paper presents a robust dynamic sliding mode control for electrically driven robot manipulators. The control law computes the motor voltages based on the voltage control strategy. Uncertainties are estimated using the Fourier series expansion and the truncation error is compensated. The Fourier coefficients are tuned based on the stability analysis. The contribution of this paper is designing a robust controller using a novel adaptive Fourier series expansion. In comparison with previous related works based on the Fourier series expansion, the superiority of this paper is presenting an adaptation law for the fundamental frequency of the Fourier series expansion and, consequently, relaxing the need for trial and error procedure in its tuning. The case study is a SCARA (Selective Compliant Articulated Robot for Assembly) robot manipulator actuated by permanent magnet DC electrical motors. The influence of uncertainty estimation based on the Fourier series expansion instead of using sign function is studied. Simulation results verify robust and satisfactory performance of the proposed controller.

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“…In this case, how to reduce the static error of fuzzy control deserves our attention [20]. In literature [21], fuzzy rules are streamlined to reduce the static error, but it will cause the number of rules to skyrocket, without significantly positive performance. In literature [22], different resolutions of fuzzy sets for different error levels are used to reduce the static error.…”

Section: Introductionmentioning

confidence: 99%

Sliding Mode Variable Structure Control for Surface Permanent Magnet Synchronous Motors Based on a Fuzzy Exponential Reaching Law

Liu

Yan

2019

Mathematical Problems in Engineering

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In order to handle heavy chattering and negative robustness caused by time-varying system parameters and external load disturbance of the speed control system, thereby having high-precision control over surface permanent magnetic synchronous machine (PMSM), this paper combines the advantages of sliding mode variable structure control (SMVSC) and fuzzy control. Firstly, a fuzzy sliding mode variable structure control (FUZZY-SMVSC) method is proposed, based on the vector control foundation framework of surface PMSM (SPMSM). It effectively reduces chattering while keeping sliding mode, according to the fuzzy rules formulated based on fuzzy control principle. Secondly, integral sliding mode surface is used and integration element is introduced into fuzzy control input, thereby reducing the static error of the conventional fuzzy control. Simulation and experimental results show that the proposed fuzzy sliding mode variable structure control reduces chattering by fuzzy reasoning and softening. Also, it still can maintain the strong robustness of sliding mode variable structure and ensures the fine dynamics of the system, against time-varying system parameters and external load disturbance.

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Intelligent Sliding-Mode Position Control Using Recurrent Wavelet Fuzzy Neural Network for Electrical Power Steering System

Cited by 27 publications

References 30 publications

Design of an adaptive dynamic sliding mode control approach for robotic systems via uncertainty estimators with exponential convergence rate

Design of an adaptive dynamic sliding mode control approach for robotic systems via uncertainty estimators with exponential convergence rate

Robust dynamic sliding mode control of robot manipulators using the Fourier series expansion

Sliding Mode Variable Structure Control for Surface Permanent Magnet Synchronous Motors Based on a Fuzzy Exponential Reaching Law

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