Adaptive Neural Control for a Class of Nonlinear Time-Varying Delay Systems With Unknown Hysteresis

• Published in: IEEE transaction on neural networks and learning systems 2014-12, Vol.25 (12), p.2129-2140
• Main Authors: Zhi Liu, Guanyu Lai, Yun Zhang, Xin Chen, Chen, Chun Lung Philip
• Format: Article
• Language: English
• Subjects: Adaptation; Adaptation models; Adaptive control systems; Adaptive neural control; Adaptive systems; Adjustable; Algorithms; Approximation methods; Artificial neural networks; Bouc-Wen hysteresis; Control systems; Hysteresis; Mathematical models; Neural networks; Neural Networks (Computer); nonlinear control; Nonlinear Dynamics; Nonlinear systems; nonstrict-feedback system; Time Factors; unknown direction hysteresis
• ISSN: 2162-237X; 2162-2388
• DOI: 10.1109/TNNLS.2014.2305717

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.