Optimal trajectory exploration large-scale deep reinforcement learning tuned optimal controller for proton exchange membrane fuel cell |
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| Institution: | 1. School of Artificial Intelligence, Shenyang University of Technology, Shenyang, Liaoning, 110870, China;2. School of Automation, Nanjing University of Science and Technology, Nanjing, Jiangsu, 210094, China;1. Department of Applied Mathematics, Bharathiar University, Coimbatore 641046, India;2. Department of Communications and Networks Engineering, Prince Sultan University, Saudi Arabia;1. Department of Automation, North University of China, Taiyuan 030051, China;2. School of Data Science and Technology, North University of China, Taiyuan 030051, China;3. Department of Transportation Engineering, North University of China, Taiyuan 030051, China |
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| Abstract: | To accurately regulate hydrogen flow and guarantee satisfactory output voltage control performance, taking advantage of the high adaptability and robustness of large-scale deep reinforcement learning, an optimal fractional-order proportion integral differential (FOPID) controller for controlling proton exchange membrane fuel cell (PEMFC) output voltage is proposed in this paper. In addition, an optimal trajectory exploration large-scale multi-delay deep deterministic policy gradient (OTEL-MD3PG) algorithm, which naturally considers the baseline FOPID coefficients in the design objective and provides the online coefficient adjusting ability through learning, is designed as the tuner of the controller to improve adaptability and robustness. This algorithm adopts the optimal trajectory exploration policy, whereby a new agent (demonstrator) generates demonstration samples that instruct the agent to learn, and another agent (tracker) adds noise to the action of the demonstrator to explore the limits of its control trajectory, thereby obtaining a more robust control strategy. The simulation results show that this proposed algorithm offers a rapid response, strong anti-interference, and excellent control performance. |
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