Adaptive Neural Backstepping Control for a Class of Strict Feedback Nonlinear Full-State Constrained System with Sensor and Actuator Faults

The aim of the current article is dealing with the adaptive neural fault tolerant control subject for a class of strict feed-back nonlinear full state constrained systems with faults in actuators and sensors. The faults which are taken into account in the current study are bias, drift, loos of accuracy, and misfortune of impression faults. In order to reduce the computational effort, only one parameter law is updated at each step. Besides, it is guaranteed that the states stay inside their constraint sets based on Barrier Lyapaunov Functions (BLF). In order to reach stability and tracking performance of the system, the controller parameter adaptive law was designed according to Lyapunov stability theory. It was found that, the Lyapunov theory demonstrates that the devised method can guarantee the closed loop stability of the control system, and all signals within the closed-loop framework are semi-globally uniformly bounded and the boundary of states are never damaged and the following blunder can converge to small desired value by the proper choose of design parameters. The simulation study have shown that the proposed control strategy was proven to be effective.