Adaptive Neural Tracking Control of Nonlinear Nonstrict-Feedback Systems With Unmodeled Dynamics

In this paper, an adaptive neural control approach for a class of nonstrict-feedback nonlinear systems with unmodeled dynamic is presented. During the controller design process, the main difficulties arise from unknown functions and unmodeled dynamics, which are inevitable in practical applications. The unknown functions are approximated by utilizing the radial basis function neural networks' (RBF NNs) method, and for the problem of the unmodeled dynamics, a dynamic signal is introduced. The innovation of this paper is that we use the property of Gaussian functions to deal with the nonstrict-feedback form. Based on the above precondition, an adaptive NNs controller design scheme is developed by applying the backstepping recursive design. The proposed adaptive control approach guarantees that all the signals in closed-loop system are semi-globally uniformly ultimately bounded (SGUUB), and the tracking error converges to a small neighborhood around the origin by choosing appropriate parameters. In the end, a simulation example is provided to demonstrate the effectiveness of the proposed method.