Application of RBF neural networks in robust adaptive DSC design of nonlinear systems

This paper offers a robust adaptive control based dynamic surface control (DSC) for uncertain nonlinear systems in which the unknown nonlinearities are not linearly parameterized with respect to uncertain parameters. Therefore, radial basis function (RBF) neural networks (NNs) are used for approximating uncertain nonlinearities. The proposed controller guarantees uniformly ultimately boundedness of the closed loop system and guarantees much higher tracking accuracy compared with the previous works in backstepping and DSC methods. Simulation results are presented to show the effectiveness of the proposed approach.