首页|Observer-based adaptive neural tracking control for a class of nonlinear systems with prescribed performance and input dead-zone constraints
Observer-based adaptive neural tracking control for a class of nonlinear systems with prescribed performance and input dead-zone constraints
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NSTL
Elsevier
? 2021 Elsevier LtdThis paper investigates the problem of output feedback neural network (NN) learning tracking control for nonlinear strict feedback systems subject to prescribed performance and input dead-zone constraints. First, an NN is utilized to approximate the unknown nonlinear functions, then a state observer is developed to estimate the unmeasurable states. Second, based on the command filter method, an output feedback NN learning backstepping control algorithm is established. Third, a prescribed performance function is employed to ensure the transient performance of the closed-loop systems and forces the tracking error to fall within the prescribed performance boundary. It is rigorously proved mathematically that all the signals in the closed-loop systems are semi-globally uniformly ultimately bounded and the tracking error can converge to an arbitrarily small neighborhood of the origin. Finally, a numerical example and an application example of the electromechanical system are given to show effectiveness of the acquired control algorithm.
Dynamic surface controlInput dead-zoneNeural networkOutput feedbackPrescribed performance control
Zong G.、Wang Y.、Karimi H.R.、Shi K.
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School of Control Science and Engineering Tiangong University
Department of Mechanical Engineering Politecnico di Milano
School of Information Science and Engineering Chengdu University
NSTL
Elsevier
