Adaptive Neural Control with Prescribed Performance for Strict-Feedback Systems with Input Saturation

This paper presents a novel adaptive control scheme that is able to achieve given tracking performance for a class of uncertain nonlinear systems in strict-feedback form with input saturation. The neural networks (NNs) are utilized to estimate the unknown nonlinearities, and an auxiliary system is designed to compensate the effect of input saturation. Different from the existing results, a novel barrier Lyapunov function is firstly introduced into the backstepping design step to deal with the tracking error performance. Therefore, it is a unified design approach for systems with or without constraint requirements. Finally, by utilizing the Lyapunov method, the boundedness of the closed-loop signals is guaranteed, and the tracking error is constrained within prescribed performance bound. The simulation results illustrate the effectiveness of the proposed control approach.