Adaptive neural dynamic surface control of MIMO nonlinear time delay systems with time-varying actuator failures

作者：Hashemi Mahnaz

来源：International Journal of Adaptive Control and Signal Processing, 2017, 31(2): 275-296.

DOI：10.1002/acs.2715

• time-varying actuator failure
• time-varying delays
• multi-input multi-output
• nonlinear systems
• dynamic surface control
• adaptive compensation

In this paper, an adaptive dynamic surface control approach is developed for a class of multi-input multi-output nonlinear systems with unknown nonlinearities, bounded time-varying state delays, and in the presence of time-varying actuator failures. The type of the considered actuator failure is that some unknown inputs may be stuck at some time-varying values where the values, times, and patterns of the failures are unknown. The considered actuator failure can cover most failures that may occur in actuators of the systems. With the help of neural networks to approximate the unknown nonlinear functions and combining the dynamic surface control approach with the backstepping design method, a novel control approach is constructed. The proposed design method does not require a priori knowledge of the bounds of the unknown time delays and actuator failures. The boundedness of all the closed-loop signals is guaranteed, and the tracking errors are proved to converge to a small neighborhood of the origin. The proposed approach is employed for a double inverted pendulums benchmark as well as a chemical reactor system. The simulation results show the effectiveness of the proposed method.

• 出版日期2017-2