Optimal dual-model controller of solid oxide fuel cell output voltage using imitation distributed deep reinforcement learning

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. (c) 2022 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

Automated summary

To address the nonlinearity and constraints in solid oxide fuel cell (SOFC) control, a dual-model control framework (DMCF) is proposed, with a PID controller and a supplementary dynamic controller. The supplementary controller adapts to uncertainties and fuel utilization constraints, while an imitation distributed deep deterministic policy gradient (ID3PG) algorithm enhances the robustness and adaptive capacity. Simulation results demonstrate the effectiveness of the proposed framework in controlling SOFC output voltage and satisfying fuel utilization constraints.