Multi-agent reinforcement learning vibration control and trajectory planning of a double flexible beam coupling system

Abstract

A multi-agent reinforcement learning vibration controller is designed for active vibration suppression of a movable double piezoelectric flexible beam coupling system, and the motion trajectory is optimized to minimize vibration excitation during motion and residual vibration after motion. The finite element method is used to model the system dynamics, then, the actual model parameters are identified by combining wavelet and intelligent optimization algorithm. The corrected piezoelectric driving model is used to train the counterfactual multi-agent reinforcement learning (COMARL) algorithm, and an excellent nonlinear controller for vibration control of piezoelectric actuators is obtained. The motion trajectory of the double flexible beam coupling system is designed by using the corrected motor-driven model. The optimal vibration suppression trajectory is obtained by using tabu search algorithm. The simulation and experimental results show that the optimized trajectory greatly reduces the vibration excitation. The controller trained by the COMARL algorithm fully considers the influence of either beam in the system, and infers the contribution of piezoelectric actuators to the completion of the overall task through counterfactual thinking. The control effect is better than that of PD control, especially the small-amplitude vibration suppression. The effectiveness of the COMARL controller is further verified by simultaneous piezoelectric control during trajectory motion. Vibrations during translational motion and at the end of motion are suppressed quickly.