Research on the compensation method for CPVSESS with friction

Abstract

As a main nonlinearity factor, the friction in the concentrating photovoltaic solar energy servo system causes a bad performances of the servo control system. In this paper, the model of the friction, which influence on the system, firstly is given. Secondly, a backstepping adaptive neural network compensation method, which integrates the backstepping method with neural network algorithm, is presented. The adaptive neural network system is used to approximate the friction, which mainly includes unknown load torque, variable system parameters and uncertain factors. Thirdly, an adaptive controller is designed based on the backstepping method, which effectively decreases the estimation error of neural network approximation system. Finally, it's reasonably proved by using Lyapunov function that the whole system is asymptotically stable. Simulation results show that the adaptive neural network controller not only restrains the friction influence and improves the track performance of CPVS, but also has strong robustness in case of the uncertain load and system parameters change.