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NEURAL NETWORK ROBUST ADAPTIVE CONTROL FOR A CLASS OF TIME DELAY UNCERTAIN NONLINEAR SYSTEMS

In this paper, a robust adaptive control scheme based on neural network is proposed for a class of time delay uncertain nonlinear systems. The system under study possesses a wide class of nonlinear time-delayed input uncertainties and function uncertainties. The radial basis function (RBF) neural networks are introduced to approximate the unknown bounded continuous input uncertainties with appropriate weight value updated laws. Based on the output of RBF neural network, a robust adaptive control scheme is developed for the time delay uncertain system. To obtain the stable closed-loop system, the parameter updated laws are presented for the approximation error of RBF neural network and the uncertain parameters of time delayed item in the input uncertainty. Finally, an example is given to illustrate the effectiveness of the proposed control scheme.