强化学习
控制理论(社会学)
稳健性(进化)
固体氧化物燃料电池
控制器(灌溉)
计算机科学
航程(航空)
数学优化
工程类
控制(管理)
数学
人工智能
基因
化学
物理化学
航空航天工程
农学
阳极
生物
生物化学
电极
标识
DOI:10.1016/j.conengprac.2021.104951
摘要
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.
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