An Improved DSC Scheme for Strict Feedback Nonlinear Systems with Unknown Control Gains

An improved DSC scheme is designed for a class of uncertain strict feedback nonlinear systems with unknown control gains. The estimation of the unknown control gain is avoided by choosing the appropriate Lyapunov function and adaptive parameters. The reliance of system tracking accuracy and stability on the time constants of the first-order low-pass filter is alleviated by using the virtual control signal to construct dynamic surface variables directly. The radial basis function neural networks (RBFNNS) are employed to approximate the unknown nonlinear functions and the adaptive compensation terms are set to eliminate the effects of the neural network approximation errors and the disturbances. Only 2 parameters need to be updated at each step by adopting the minimal learning parameter (MLP) technique. The stability of the closed-loop system is analysed by Lyapunov theorem. It is proved that all signals in the closed-loop system are semi- globally uniformly ultimately bounded. Simulation results show that compared with traditional dynamic surface control method, the improved scheme proposed in this paper has better control performance.