Backstepping Command Filter Control for Electromechanical Servo Systems with Unknown Dynamics Based on Reinforcement Learning

To address the challenges of acquiring precise dynamic models for electromechanical servo systems and the susceptibility of system state information to noise, a backstepping command filter controller based on reinforcement learning is proposed. This method can achieve high-precision and low-energy control in electromechanical servo systems subject to noise interference and unmodeled disturbances. The proposed method employs a command filter to obtain differential estimations of known signals and process noise. Reinforcement learning is employed to estimate unknown system disturbances, including unmodeled friction and external interference. The weights of the networks are updated using an online gradient descent algorithm, eliminating the need for an offline learning phase. In addition, the Lyapunov function analysis method is used to demonstrate the stability of the closed-loop system. To validate the effectiveness of the proposed method, comparative experiments were conducted using an electromechanical servo experimental platform. The experimental results indicate that, compared to other mainstream controllers, the control algorithm excels in tracking accuracy, response speed, and energy efficiency, and demonstrates significant robustness against system uncertainties and external disturbances.