Robust adaptive neural network control for strict-feedback nonlinear systems with uncertainties

In this paper, we present a robust adaptive neural network control design approach for strict-feedback nonlinear systems with uncertainties. In the controller design process, all unknown terms at intermediate steps are passed down and approximated by a single neural network at the last step. By this way, the structure of the designed controller is much simpler, and the control law and the adaptive law can be given directly. The result of stability analysis shows that the proposed scheme can guarantee the uniform ultimate boundedness of the closed-loop system signals, and the control performance can be guaranteed by an appropriate choice of the control parameters. The effectiveness of the proposed approach is demonstrated by simulation results.