Adaptive NN control for nonlinear systems with uncertainty based on dynamic surface control

Abstract

Abstract The problem of adaptive neural network (NN) control and the dynamic surface control (DSC) method for a series of nonlinear systems with uncertainty is discussed in this paper. Unknown smooth functions can be approximated by radial basis function-neural networks (RBF-NNs) with arbitrary accuracy in nonlinear systems. The DSC scheme is introduced for nonlinear systems with uncertainty to overcome the “explosion of complexity” compared with the conventional backstepping approach. Meanwhile, the global asymptotic stability of nonlinear systems is manifested via the Lyapunov stability theory, which indicates the uniform boundedness of all closed-loop signals is ensured and dynamic surface errors are arbitrarily small in the compact set by selecting proper parameters. Finally, the effectiveness of the proposed control technique is validated by two simulation examples.