A active vibration control strategy based on reinforcement learning

Concerning the uncertainty and nonlinearity of a controlled system, an active control strategy for random vibration based on reinforcement learning was proposed. The vibration controller was designed by a reinforcement learning algorithm-deep deterministic policy gradient (DDPG). This process does not involve expert experience and was entirely completed by the autonomous interactive learning of DDPG algorithms and data. The controller is a multi-layer neural network model, and this kind of controller designed by reinforcement learning algorithm is called neural network controller designed by reinforcement learning (RL-NN)controller. The performance of the RL-NN controller was verified through two numerical simulation examples: the active control effect of a single degree of freedom system with uncertainty reaches 97%. The semi-active control effect of the 1/4 vehicle suspension system with uncertainty and nonlinearity reaches 74%. The results show that the RL-NN controller has excellent vibration control capabilities for systems with uncertain and nonlinear. The random vibration active control strategy designed by the reinforcement learning algorithm in only a few hours is better than the control strategy designed by experts over few years. This provides a new approach to design active/semi-active controllers for complex systems. © 2021, Editorial Office of Journal of Vibration and Shock. All right reserved.