Optimal dual-model controller of solid oxide fuel cell output voltage using imitation distributed deep reinforcement learning |
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| Affiliation: | 1. School of Electronic and Information Engineering, Shanghai University of Electric Power, Shanghai, 201306, China;2. Department of Electrical Engineering, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, 200240 Shanghai, China;3. Shanghai Xinwei Semiconductor Co. Ltd, China;1. Advanced Instrumentation for Nano-Analytics (AINA), MRT Department, Luxembourg Institute of Science and Technology, 41 Rue Du Brill, L-4422 Belvaux, Luxembourg;2. Chair of Materials Physics, Institute of Materials Science, University of Stuttgart, Heisenbergstr. 3, 70569, Stuttgart, Germany;1. Mines Saint-Etienne, Univ Lyon, CNRS, UMR 5307 LGF, Centre SMS, F-42023, Saint-Etienne, France;2. SafeMetal, 1 Boulevard de La Boissonnette, 42110 Feurs, France;1. College of Mechanical Engineering, Hunan Institute of Science and Technology, Yueyang, 414006, China;2. State Key Laboratory of Multiphase Flow in Power Engineering, Xi''an Jiaotong University, Xi''an 710049, China;3. Shenzhen Gas Corporation Ltd., Shenzhen 518040, China |
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| Abstract: | Solid oxide fuel cell (SOFC) is disadvantaged by significant nonlinearity, which makes it difficult to control output voltage of SOFC and satisfy the constraints of fuel utilization simultaneously. In order to solve this problem, a dual-model control framework (DMCF) is proposed. In particular, there are two controllers deployed under this framework, with an PID controller and a supplementary dynamic controller to track the SOFC output voltage. The supplementary dynamic controller is conducive to the stabilization of tracking by adapting to the uncertainties, considering the constraint on fuel utilization. In addition, an imitation distributed deep deterministic policy gradient (ID3PG) algorithm, which integrates imitation learning and distributed deep reinforcement learning to enhance the robustness and adaptive capacity of this framework, is proposed for the supplementary dynamic controller. The simulation results obtained in this work have demonstrated that the proposed framework is effective in imposing control on SOFC output voltage and preventing constraint violations of fuel utilization. |
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| Keywords: | Deep reinforcement learning Imitation distributed deep deterministic policy gradient algorithm (ID3PG) Solid oxide fuel cell Output voltage control Fuel utilization Optimal control framework |
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