Adaptive Control Design for High-order MIMO Nonlinear Time-delay Systems Based on Neural Network

An adaptive controller for a class of high-order MIMO nonlinear time-delay systems in block-triangular form is proposed in the paper. The radial basis function neural network is chosen to approximate the unknown nonlinear functions in the system dynamics at first. Lyapunov–Krasovskii functionals are used to compensate the influence of delay terms. Then an adaptive neural network output tracking controller is designed by using the back-stepping recursive method. Based on Lyapunov stability theory, the proposed controller can guarantee all closed-loop signals are globally, uniformly and ultimately bounded, while the output tracking is able to converge to a neighborhood of the origin. Finally, a simulation example is given to illustrate the correctness of the theoretical results.