Optimal adaptive control for solid oxide fuel cell with operating constraints via large-scale deep reinforcement learning

Abstract

Since a solid oxide fuel cell (SOFC) is a complicated nonlinear, time-varying and constrained system, it is difficult to control the fuel flow to stabilize the output voltage while considering fuel utilization operating constraints. To overcome this problem, an adaptive fractional-order proportional integral derivative (FOPID) controller, taking advantage of the adaptability and model-free features of large-scale deep reinforcement learning, is proposed in this paper. Furthermore, a fittest survival strategy large-scale twin delayed deep deterministic policy gradient (FSSL-TD3) algorithm is designed as the tuner of this controller. In this algorithm, the exploration efficacy is improved by way of the fittest survival strategy and imitation learning. Other techniques are also applied to this algorithm in order to improve the robustness of FOPID controller. In addition, by formulating the reward function of the FSSL-TD3 algorithm, the fuel utilization of the SOFC can always be kept in a safe range, which is not possible for conventional control algorithms. The simulation results in this paper show that the output voltage of SOFCs can be controlled effectively by this controller while fuel utilization is retained within a reasonable range.