Adaptive neural control for nonlinear systems with sensor fault and input nonlinearities

In this work, the problem of adaptive neural control for nonlinear systems with input nonlinearities and sensor fault in non-strict feedback form is addressed. Input saturation and dead-zone exist in the nonlinear system simultaneously. The impact of external disturbances and sensor fault in nonlinear systems is considered. To approximate the unknown functions in the system, radial basis function neural networks are used. An adaptive neural controller is developed by using the approximation ability of the neural networks and the backstepping method. Based on Lyapunov stability theory, the proposed control method ensures that all signals in the closed-loop system are semi-globally uniformly ultimately bounded and that the output of the system follows the reference signal within a bounded error. Finally, to demonstrate the effectiveness of the proposed control method, one numerical example and a real-life example about an electromechanical system are provided.

Automated summary

This work addresses the problem of adaptive neural control for nonlinear systems with input nonlinearities and sensor fault in non-strict feedback form. Radial basis function neural networks are used to approximate the unknown functions in the system, and an adaptive neural controller is developed using the backstepping method. The proposed control method ensures the stability of the system and the tracking performance of the output based on Lyapunov stability theory.