Adaptive-backstepping control for servo system based on recurrent-neural-network

For the problem of parameter variation and nonlinear dynamic friction compensation, a recurrent-neural-network (RNN)-based adaptive-backstepping control (RNABC) for servo system was proposed. The RNABC system is comprised of a backstepping controller and a robust controller. The backstepping controller containing an RNN uncertainty observer is the principal controller, and the robust controller is designed to dispel the effect of approximation error introduced by the uncertainty observer. The adaptation laws of the adaptive-backstepping approach are derived in the sense of the Lyapunov function, thus, the stability of the system can be guaranteed. Simulation results verify that the proposed RNABC can achieve favorable tracking performance for servo system, even regard to parameter variations and friction disturbance.