1.Beijing University of Technology;2.Beijing Key Lab of Earthquake Engineering and Structural Retrofit,Beijing University of Technology
Three variable control has commonly used as the underlying control algorithm in shaking table. However, the process of parameter tuning in three-variable control involves numerous parameters, and traditional parameter tuning methods suffer from problems such as low efficiency and complicated processes. In order to improve tuning efficiency and accuracy, a novel parameter tuning method for three variable control of shaking table based on the DDPG algorithm was proposed. The method builds a discrete model of shaking table and treats it as a reinforcement learning environment. By using the DDPG algorithm to learn and train the state-action-reward of the system, the optimal control parameters were obtained. The tuning parameters were then tested in shaking table and compared with traditional tuning methods. The results showed that the DDPG algorithm could effectively optimize the control performance of the shaking table, improve the accuracy and reliability of experimental results, and has practical application value.