基于分数阶自适应神经网络的电动舵机伺服系统摩擦干扰补偿控制OA北大核心CSTPCD

Friction torque disturbance affects the tracking performance of electromechanical actuator servo system,bringing position and speed tracking errors,and even may leading to instability of the servo system.Ai-ming at the problem of poor tracking performance of electromechanical actuator servo system under friction torque disturbance,a FOANN friction compensation algorithm is proposed to estimate and compensate the friction torque.Firstly,base on LuGre friction model,a electromechanical actuator model is established,and the un-measured state variable in the LuGre model is estimated by radial basis function neural network.Secondly,a FOANN controller is designed,and the stability of corresponding closed-loop system is proved by Lyapunov sta-bility theory.Finally,through simulation and experimental platform,the dynamic performance of FOANN is compared with those of traditional PD and MRAC.The simulation and experimental results show that,with the proposed FOANN friction torque compensation algorithm,the tracking errors of both position and velocity of electromechanical actuator servo system are greatly reduced.FOANN algorithm can effectively estimate and com-pensate friction torque,reduce the impact of friction disturbance and enhance the dynamic performance of the servo system.