期刊
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
卷 26, 期 8, 页码 1789-1802出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2015.2420661
关键词
Adaptive control; barrier Lyapunov function (BLF); Bouc-Wen hysteresis model; neural networks (NNs)
类别
资金
- National Natural Science Foundation of China [U1134004]
- Natural Science Foundation of Guangdong Province for Distinguished Young Scholars [S20120011437]
- Ministry of Education through the New Century Excellent Talent [NCET-12-0637]
- 973 Program of China [2011CB013104]
- Ministry of Education of China [20124420130001]
This paper addresses the problem of adaptive neural output-feedback control for a class of special nonlinear systems with the hysteretic output mechanism and the uneasured states. A modified Bouc-Wen model is first employed to capture the output hysteresis phenomenon in the design procedure. For its fusion with the neural networks and the Nussbaum-type function, two key lemmas are established using some extended properties of this model. To avoid the bad system performance caused by the output nonlinearity, a barrier Lyapunov function technique is introduced to guarantee the prescribed constraint of the tracking error. In addition, a robust filtering method is designed to cancel the restriction that all the system states require to be measured. Based on the Lyapunov synthesis, a new neural adaptive controller is constructed to guarantee the prescribed convergence of the tracking error and the semiglobal uniform ultimate boundedness of all the signals in the closed-loop system. Simulations are implemented to evaluate the performance of the proposed neural control algorithm in this paper.
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