In this paper, a modelreferenceadaptiveslidingmode (MRASMC) using a radical basis function (RBF) neural network (NN) is proposed to control the single-phase active power filter (APF). The RBF NN is utilized to app...
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In this paper, a modelreferenceadaptiveslidingmode (MRASMC) using a radical basis function (RBF) neural network (NN) is proposed to control the single-phase active power filter (APF). The RBF NN is utilized to approximate the nonlinear function and eliminate the modeling error in the APF system. The modelreferenceadaptive current controller in AC side not only guarantees the globally stability of the APF system but also the compensating current to track the harmonic current accurately. Moreover, a slidingmode voltage controller based on an exponential approach law is designed to improve the tracking performance of DC side voltage. Simulation results demonstrate strong robustness and outstanding compensation performance with the proposed APF control system. In conclusion, MRASMC using REF NN can improve the adaptability and robustness of the APF system and track the given instructional signal quickly. (C) 2015 Elsevier Ltd. All rights reserved.
A new modelreference direct adaptiveslidingmodecontrol (MRDASMC) approach for electromechanical actuator (EMA) is presented. Uncertainties with parameter variation, external load disturbance, nonlinear structure a...
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ISBN:
(纸本)9781424438723
A new modelreference direct adaptiveslidingmodecontrol (MRDASMC) approach for electromechanical actuator (EMA) is presented. Uncertainties with parameter variation, external load disturbance, nonlinear structure and unmodelled dynamics in the system are treated as a lumped uncertainty. The lumped uncertainty is estimated without the bounds known in advance. A switching function is employed to compensate for the estimated error of the lumped uncertainty. While the issue of chattering in classical SMC is suppressed as the amplitude of the switching function is tuned with the tracking error by adaption law. The adaption law contains a fading factor which can prevent saturation of the control effort. The stability of the control system is guaranteed by Lyapunov approach. Simulation results show that the proposed controller can provide favorable performance and is robust to system uncertainties.
This study deals with analyze of an adaptiveslidingmodelcontroller for a class of switched linear systems in the context of modelreferenceadaptivecontrol (MRAC) using RBF neural network (RBFNN) with the aid of d...
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This study deals with analyze of an adaptiveslidingmodelcontroller for a class of switched linear systems in the context of modelreferenceadaptivecontrol (MRAC) using RBF neural network (RBFNN) with the aid of disturbance observer (DO). For this purpose, adaptive laws and switching rules are designed. These are constructed based on tracking error and slidingmodecontrol, together with using time-dependent switching conceptualizations. A DO is used to estimate the external disturbance with an adaptive RBFNN which is applied to obtain the external disturbance upper bound estimation, combined with an adaptiveslidingmodecontrol (ASMC) under the identic Lyapunov stability framework. The switching rules are based on dwell time (DT) and average dwell time (ADT) switching. The ASMC updates the system dynamics so that it assures the proposed closed-loop switched linear system stability via fast switching, resulting in the form of globally uniformly ultimately bounded (GUUB) stability. The convergence of the process of updating the weights in the adaptive RBFNN and the boundedness of updated estimates of weights are satisfied. Achieving the state tracking, robustness, reducing the chattering problem and anti-disturbance performance are the main objectives. Moreover, switching rules based on the mode-dependent approaches have been developed, which can allow faster switching as compared to switching rules based on the DT and ADT. Finally, to evaluate the efficiency of the obtained theoretical results, the controller and the proposed method have been tested on the electro-hydraulic system (EHS).
A new modelreference direct adaptiveslidingmodecontrol(MRDASMC) approach for electromechanical actuator(EMA) is *** with parameter variation,external load disturbance,nonlinear structure and unmodelled dynamics in...
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A new modelreference direct adaptiveslidingmodecontrol(MRDASMC) approach for electromechanical actuator(EMA) is *** with parameter variation,external load disturbance,nonlinear structure and unmodelled dynamics in the system are treated as a lumped *** lumped uncertainty is estimated without the bounds known in advance.A switching function is employed to compensate for the estimated error of the lumped *** the issue of chattering in classical SMC is suppressed as the amplitude of the switching function is tuned with the tracking error by adaption *** adaption law contains a fading factor which can prevent saturation of the control *** stability of the control system is guaranteed by Lyapunov *** results show that the proposed controller can provide favorable performance and is robust to system uncertainties.
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