Adaptive Neural Globally Asymptotic Tracking Control for a Class of Uncertain Nonlinear Systems

This paper investigates the adaptive neural tracking control problem for strict-feedback nonlinear systems. Superior to the existing results that only semi-globally uniformly ultimately bounded stability can be achieved, each virtual and actual controller of the proposed design switches between an adaptive neural controller and a robust controller, ensuring a globally uniform ultimate boundedness. The overall controller will guarantee the asymptotic tracking performance under the neural network approximation framework. This is accomplished by using a new control strategy, where a proportional-integral compensator that can be conveniently implemented in practice is introduced. Meanwhile, a novel Lyapunov function is developed with the dynamic surface control, whose set-valued Lie derivative will be used to construct the desired controllers and adaptive laws. Finally, the simulation results are given to show the advantages and effectiveness of the proposed new design technique.