Adaptive dynamic surface control for perturbed nonlinear time-delay systems using neural network

This paper investigates the adaptive neural network (NN) control problem for a class of perturbed strict-feedback nonlinear systems with unknown time delays. Radial basis function (RBF) neural networks are used to approximate unknown nonlinear functions. By constructing appropriate Lyapunov-Krasovskii functionals, the unknown time delay terms have been compensated. Dynamic surface control (DSC) technique is used to overcome the problem of “explosion of complexity” in backstepping design procedure. In addition, the semiglobally uniformly ultimate boundedness of all the signals in the closed-loop system is proved. Simulation study has been conducted to show the effectiveness of the proposed scheme.