Event-triggered adaptive neural control for uncertain nonstrict-feedback nonlinear systems with full-state constraints and unknown actuator failures

This paper investigates an adaptive asymptotic tracking controller for uncertain nonstrict-feedback nonlinear systems(NFNSs) with full-state constraints and unknown actuator failures. By constructing a class of new switched high-order barrier Lyapunov functions(BLFs) candidates to the backstepping design, the asymptotic tracking performance can be guaranteed, all states do not violate constraints. The neural networks(NNs) is used to handle the difficulties caused by the unknown nonlinearities, the event-triggered control law based on relative threshold method for reducing the communication load between the controller and actuator. The control scheme guarantees that all closed-loop signals of the system are bounded and the tracking error asymptotically approaches a pre-defined bound, no matter whether the actuators operate in normal or faulty modes and the number of actuator failures can be infinite. Finally, simulation results including numerical simulation and practical application simulation verify the feasibility of the control scheme.