Adaptive Neural Tracking Control for Switched Nonlinear Disturbed Systems with Non-lower Triangular Structure

In this paper, an adaptive neural tracking control frame is structured for a class of uncertain switched nonlinear systems with non-lower triangular structure and disturbances. A first-order filter is designed in each step of backstepping to avoid the question of “explosion of complexity” and radial basis function (RBF) neural networks (NNs) are employed to deal with the unknown nonlinear terms. Meanwhile, the design difficulties caused by disturbances are solved by some classical mathematical techniques. Moreover, it can be proved that all of the signals of the closed-loop system are semi-global uniformly ultimately boundedness (SGUUB) and the tracking error remains in a small neighborhood of zero. Finally, an example is given to demonstrate the effectiveness of the presented control scheme.