Predefined-time tracking control for a class of strict-feedback nonlinear systems with time-varying actuator failures and input delay

This paper investigates the problem of predefined-time tracking control for a class of strict-feedback nonlinear systems with time-varying actuator failures and input delay. First, the input-delay problem is dealt with by Padé approximation. Second, the radial-based neural network technique is used to deal with the uncertainties and unknown disturbances in the system, and then the dynamic surface combined with the command filtering technique effectively solves the “complexity explosion” problem in the design of the controller for the high-order system. In order to eliminate the filtering error and obtain more stable control performance, a novel predefined-time compensation mechanism for the filtering error is designed. Again, the impact of time-varying actuator failures on the system stability is efficiently handled by designing a reasonable predefined-time control strategy, so that the controlled system achieves tracking stability within a predefined time. Finally, the effectiveness and superiority of the predefined-time control strategy proposed in this paper are verified through numerical simulation and its comparative experiments.