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Solid oxide fuel cells (SOFC) are complex nonlinear and time-varying systems with operational constraints. How to effectively control and stabilize the output voltage while preventing constraint violations becomes a main challenge to its wide application. To improve the efficiency of its operation and power tracking control and prevent constraint violations, this paper designs a data-driven adaptive proportional integral derivative (PID) controller, which maintains the output voltage at the reference value via the optimal control of the hydrogen flow. Moreover, a novel large-scale deep reinforcement learning (DRL) algorithm, called the two-stage training strategy large-scale twin delayed deep determination policy gradient (TGSL-TD3PG), is adopted to adaptively adjust the baseline coefficients of the designed controller scaffolded by the high adaptability and model-free features of reinforcement learning. In the training of …