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  • Open Access


    Deep Deterministic Policy Gradient to Regulate Feedback Control Systems Using Reinforcement Learning

    Jehangir Arshad1, Ayesha Khan1, Mariam Aftab1, Mujtaba Hussain1, Ateeq Ur Rehman2, Shafiq Ahmad3, Adel M. Al-Shayea3, Muhammad Shafiq4,*

    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1153-1169, 2022, DOI:10.32604/cmc.2022.021917

    Abstract Controlling feedback control systems in continuous action spaces has always been a challenging problem. Nevertheless, reinforcement learning is mainly an area of artificial intelligence (AI) because it has been used in process control for more than a decade. However, the existing algorithms are unable to provide satisfactory results. Therefore, this research uses a reinforcement learning (RL) algorithm to manage the control system. We propose an adaptive speed control of the motor system based on depth deterministic strategy gradient (DDPG). The actor-critic scenario using DDPG is implemented to build the RL agent. In addition, a framework has been created for traditional… More >

  • Open Access


    A Progressive Output Strategy for Real-time Feedback Control Systems

    Qiming Zou1, Ling Wang1, *, Jie Liu1, Yingtao Jiang2

    Intelligent Automation & Soft Computing, Vol.26, No.3, pp. 631-639, 2020, DOI:10.32604/iasc.2020.012549

    Abstract The real-time requirements imposed on a feedback control system are often hard to be met, as the controller spends a disproportionately large amount of time waiting for a control cycle to reach its final state. When such a final state is established, multiple tasks have to be prioritized and launched altogether simultaneously, and the system is given an extremely short time window to generate its output. This huge gap between the wait and action times, perceived as a load unbalancing problem, hinders a control decision to be made in real time. To address this challenging problem, in this paper, we… More >

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