Adaptive neural network control for strict-feedback nonlinear systems using backstepping design

This paper focuses on the adaptive control problem of strict-feedback nonlinear systems using multilayer neural networks (MNNs). By introducing a modified Lyapunov function, a smooth and singularity-free adaptive controller is first designed for a first-order plant. Then, an extension is made to high-order nonlinear systems using backstepping design. The control scheme developed guarantees the uniform ultimate boundedness of the closed-loop adaptive systems. The relationship between the transient performance and the design parameters is given to guide the tuning of the controller.