Adaptive single neural network control for a class of uncertain discrete-time nonlinear strict-feedback systems with input saturation

摘要: In this paper, a novel adaptive neural network control approach is presented for class of uncertain discrete-time nonlinear strict-feedback systems with input saturation. By combining single approximation and minimal learning parameter technique, the proposed able to eliminate complexity growing problem alleviate explosion parameters. An auxiliary design system incorporated into scheme overcome saturation constraints. Following approach, designed controller contains only one actual law law, numbers variables weights updated online are decreased drastically, number whole reduced one. Compared existing methods, mechanism much simpler structure parameterization achieved; therefore, computational burden lighter. It shown via Lyapunov theory that all signals in closed-loop uniformly ultimately bounded. Finally, simulation results two examples employed illustrate effectiveness merits scheme.