An NN-Based Robust Adaptive Control Approach for a Class of Uncertain Strict-Feedback Nonlinear Systems

A robust adaptive neural network control approach is presented for a class of uncertain strict-feedback nonlinear systems with unknown dead-zone and disturbances. In the controller design, a single neural network is used to approximate the lumped unknown part of the system. By the approach, only one actual control law is implemented at the last step, and all the virtual control laws at intermediate steps need not be implemented actually. Thus, the designed controller is simpler in structure. Furthermore, the actual control law and one adaptive law can be given directly for the class of systems under study. The result of stability analysis shows that the proposed scheme can guarantee the uniform ultimate boundedness of all the closed-loop system signals, and the steady-state tracking error can be made arbitrarily small by appropriately choosing control parameters. A simulation example is given to demonstrate the effectiveness and merits of the proposed approach.