Adaptive control for a class of nonlinear systems with unknown control gains and input saturation

In this paper, an adaptive neural network (NN) asymptotic tracking control strategy is proposed for a class of nonlinear systems with unknown control gains and external disturbances under asymmetric input saturation constraint. Based on backstepping technique and command filter, a new controller containing the Nussbaum-type function is designed. Then the NN with minimal learning parameter is adopted to online approximate the unknown nonlinear function, where the unknown ideal weight vector and the approximation error are estimated by adaptive laws. Additionally, a new inequality is utilized to guarantee the desired output can be tracked asymptotically. The results show that all signals are bounded in the closed-loop system. Finally, the simulation result demonstrates the effectiveness of our proposed control scheme.