Neuroadaptive Fault-Tolerant Control for Uncertain Nonlinear Systems with New Time-Varying Constraints on States

In this article, a neuroadaptive fault-tolerant control scheme is proposed for a class of nonlinear systems with actuator failures, full state constraints and unknown time-varying disturbances. Besides the constraint boundary is different from the common research which is connected with time, partial states and desired trajectory. Additionally, we employ neural networks to approximate the unknown nonlinear functions, and the problem of "explosion of complexity" is avoided by utilizing the dynamic surface control technique during the design process of conventional backstepping control. The control scheme can guarantee the ultimate bounded stable tracking and the full state constraints are not violated. Both the theoretical analysis and numerical simulation are presented to demonstrate the feasibility and effectiveness of the proposed method.