Adaptive Dynamic Surface Control of a Class of Nonlinear Systems With Unknown Direction Control Gains and Input Saturation

In this paper, adaptive neural network based dynamic surface control (DSC) is developed for a class of nonlinear strict-feedback systems with unknown direction control gains and input saturation. A Gaussian error function based saturation model is employed such that the backstepping technique can be used in the control design. The explosion of complexity in traditional backstepping design is avoided by utilizing DSC. Based on backstepping combined with DSC, adaptive radial basis function neural network control is developed to guarantee that all the signals in the closed-loop system are globally bounded, and the tracking error converges to a small neighborhood of origin by appropriately choosing design parameters. Simulation results demonstrate the effectiveness of the proposed approach and the good performance is guaranteed even though both the saturation constraints and the wrong control direction are occurred.