期刊
NEUROCOMPUTING
卷 284, 期 -, 页码 17-26出版社
ELSEVIER
DOI: 10.1016/j.neucom.2018.01.023
关键词
Adaptive tracking control; Input saturation; Neural network (NN); Output feedback; Prescribed performance; Unknown control direction
资金
- National Natural Science Foundation of China [61621004, 61703429, 61420106016]
- Research Fund of State Key Laboratory of Synthetical Automation for Process Industries [2013ZCX01]
In this paper, the problem of observer-based adaptive tracking control is investigated for a class of non-linear systems with unknown control direction, input saturation and tracking error constraint. The Nuss-baum function is employed to address the unknown control direction and a state observer is constructed by neural networks (NNs) to estimate the unmeasurable states. A new error constraint transformation is proposed to guarantee that the tracking error satisfies the prescribed performance. Then, a novel adaptive prescribed performance neural network (NN) output feedback tracking control method is designed. It is proved that the designed controller can guarantee the boundedness of all the signals in the closed-loop system and the prescribed time-varying tracking performance. Finally, simulations on two examples are performed to illustrate the efficiency of the proposed control method. (C) 2018 Elsevier B.V. All rights reserved.
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