Backstepping-based neural adaptive control for saturated nonlinear systems

In this paper, neural adaptive backstepping control is investigated for a class of nonlinear systems with a saturated control input. To deal with the tracking problem in the face of input saturation, effective auxiliary systems are constructed, which generate signals preventing the stability of the closed-loop system, and the learning capabilities of adaptation laws from being destroyed. Radial basis function neural networks (RBF NNs) are used in the online learning of unknown dynamics. The semi-global bounded stability of the closed-loop system under the proposed control law is guaranteed by utilizing Lyapunov stability theory, and the system output tracks the desired curve with only small error. Simulation results demonstrate the effectiveness of the proposed control scheme.