Indirect adaptive neural network block control using dynamic surface control

Based on dynamic surface control, a novel design scheme of adaptive neural network controller is proposed for a class of MIMO nonlinear systems which could be turned to 'standard block control type', without inverse gain matrix in this paper. The problem of explosion of complexity in traditional backstepping design, which is caused by repeated differentiations of certain nonlinear functions such as virtual control, is overcome by introducing the first order filter. Moreover, the possible controller singularity in feedback linearization is avoided without projection algorithm. Using Lyapunov method, the closed-loop systems is shown to be semi-globally uniformly ultimately bounded, with tracking error converging to a small neighborhood of origin by appropriately choosing design constants. Simulation results demonstrate the effectiveness of the proposed method.