Adaptive dynamic surface control of nonlinear systems with perturbed uncertainties in strict-feedback form

Based on the approximation capability of radial basis neural networks and the integral-type Lyapunov function, adaptive dynamic surface control(DSC) is investigated for a class of strict-feedback nonlinear systems with unknown virtual control gain functions. The main advantages of the proposed scheme are that only one parameter is adjusted in the whole backstepping design by using Young's inequality and dynamic surface control, and the computational burden is effectively alleviated. By theoretical analysis, the closed-loop control system is proved to be semi-globally uniformly ultimately bounded, with arbitrary small tracking error by appropriately choosing design constants. Simulation results demonstrate the effectiveness of the proposed method.