Journal
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
Volume 25, Issue 12, Pages 2129-2140Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2014.2305717
Keywords
Adaptive neural control; Bouc-Wen hysteresis; nonlinear control; nonstrict-feedback system; unknown direction hysteresis
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Funding
- National Natural Science Foundation of China [60974047, U1134004]
- Natural Science Foundation of Guangdong Province [S2012010008967]
- Science Fund for Distinguished Young Scholars [S20120011437]
- Zhujiang New Star
- Ministry of education of New Century Excellent Talent [NCET-12-0637]
- 973 Program of China [2011CB013104]
- Ministry of Education of China [20124420130001]
- University of Macau Multiyear Research Grants
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This paper investigates the fusion of unknown direction hysteresis model with adaptive neural control techniques in face of time-delayed continuous time nonlinear systems without strict-feedback form. Compared with previous works on the hysteresis phenomenon, the direction of the modified Bouc-Wen hysteresis model investigated in the literature is unknown. To reduce the computation burden in adaptation mechanism, an optimized adaptation method is successfully applied to the control design. Based on the Lyapunov-Krasovskii method, two neural-network-based adaptive control algorithms are constructed to guarantee that all the system states and adaptive parameters remain bounded, and the tracking error converges to an adjustable neighborhood of the origin. In final, some numerical examples are provided to validate the effectiveness of the proposed control methods.
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