Single neural network approximation-based adaptive control of a class of uncertain strict-feedback nonlinear systems

A single neural network approximation-based adaptive control design approach is presented for a class of uncertain strict-feedback nonlinear systems. In the controller design, all the unknown terms at intermediate steps are passed down and approximated by a single neural network at the last step. By this way, the controller consisting of an actual control law and an adaptive law can be given directly. The result of stability analysis shows that all the closed-loop system signals are uniformly ultimately bounded, 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 of the proposed scheme.