Adaptive neural dynamic surface control of nonlinear time delay systems

In this paper, an adaptive Dynamic Surface Control approach is developed for a class of nonlinear time delay systems with unknown nonlinear functions, control gain and bounded time varying state delays. With the help of Neural Networks to approximate the unknown nonlinear functions and Combining the Dynamic Surface Control approach with the backstepping design method, a novel adaptive neural control approach is constructed. The proposed design method does not require a priori knowledge of the bounds of the time delays. The boundedness of all the closed-loop signals is guaranteed and the tracking error is proved to converge to a small neighborhood of the origin. The proposed approach is employed for a time delay plant as well as a two-stage chemical reactor with delayed recycle streams. The simulation results verify the effectiveness of the proposed adaptive control approach.