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Neural Network Robust Tracking Control with Adaptive Critic Framework for Uncertain Nonlinear Systems
Dec 21, 2017Author:
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Title: Neural Network Robust Tracking Control with Adaptive Critic Framework for Uncertain Nonlinear Systems

 Authors: Wang, D; Liu, DR; Zhang, Y; Li, HY

 Author Full Names: Wang, Ding; Liu, Derong; Zhang, Yun; Li, Hongyi

 Source: NEURAL NETWORKS, 97 11-18; 10.1016/j.neunet.2017.09.005 JAN 2018

 Language: English

 Abstract: In this paper, we aim to tackle the neural robust tracking control problem for a class of nonlinear systems using the adaptive critic technique. The main contribution is that a neural-network-based robust tracking control scheme is established for nonlinear systems involving matched uncertainties. The augmented system considering the tracking error and the reference trajectory is formulated and then addressed under adaptive critic optimal control formulation, where the initial stabilizing controller is not needed. The approximate control law is derived via solving the Hamilton-Jacobi-Bellman equation related to the nominal augmented system, followed by closed-loop stability analysis. The robust tracking control performance is guaranteed theoretically via Lyapunov approach and also verified through simulation illustration. (C) 2017 Elsevier Ltd. All rights reserved.

 ISSN: 0893-6080

 eISSN: 1879-2782

 IDS Number: FO0RI

 Unique ID: WOS:000416454000003

 PubMed ID: 29031083

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