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首页> 外文期刊>IEEE Transactions on Industrial Electronics >Dual-Rate Operational Optimal Control for Flotation Industrial Process With Unknown Operational Model
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Dual-Rate Operational Optimal Control for Flotation Industrial Process With Unknown Operational Model

机译：未知运行模型的浮选工业过程双速率最优操作控制

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摘要

This paper studies the two-timescales operational optimal control problem for the flotation industrial process with unknown operational model in the presence of setpoint constraints on the device layer. A novel dual-rate data-driven algorithm based on lifting technology and reinforcement learning (RL) is proposed. First, a dual-rate flotation process model including the device layer and the operational models is formulated. Then, a proportional integral (PI) controller for device layer is designed, by using lifting technology, a unified timescale controlled plant model with partially unknown dynamics is established. Based on such a model, an online learning algorithm using neural network is presented so that the operational indices, namely concentrate and tail grades, can be kept in the target range while maintaining the setpoints of the device layer within the specified bounds. At last, emulation experiments in a hardware-in-the-loop system are used to verify the effectiveness of the proposed method.
机译：本文研究了在设备层存在设定值约束的情况下，具有未知运行模型的浮选工业过程的两阶段运行最优控制问题。提出了一种基于提升技术和强化学习的双速率数据驱动算法。首先，建立了包括设备层和操作模型的双速率浮选过程模型。然后，通过提升技术设计了一种用于设备层的比例积分（PI）控制器，建立了一个具有部分未知动力学的统一时标控制工厂模型。在这种模型的基础上，提出了一种使用神经网络的在线学习算法，以便在将设备层的设定值保持在指定范围内的同时，将操作指标（即精矿和尾料等级）保持在目标范围内。最后，通过在硬件在环系统中的仿真实验，验证了该方法的有效性。

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  • 来源
    《IEEE Transactions on Industrial Electronics》 |2019年第6期|4587-4599|共13页
  • 作者

    Jiang Yi; Fan Jialu; Chai Tianyou; Lewis Frank L.;

  • 作者单位

    Northeastern Univ, State Key Lab Synthet Automat Proc Ind, Shenyang 110819, Liaoning, Peoples R China|Northeastern Univ, Int Joint Res Lab Integrated Automat, Shenyang 110819, Liaoning, Peoples R China;

    Northeastern Univ, State Key Lab Synthet Automat Proc Ind, Shenyang 110819, Liaoning, Peoples R China|Northeastern Univ, Int Joint Res Lab Integrated Automat, Shenyang 110819, Liaoning, Peoples R China;

    Northeastern Univ, State Key Lab Synthet Automat Proc Ind, Shenyang 110819, Liaoning, Peoples R China|Northeastern Univ, Int Joint Res Lab Integrated Automat, Shenyang 110819, Liaoning, Peoples R China;

    Northeastern Univ, State Key Lab Synthet Automat Proc Ind, Shenyang 110819, Liaoning, Peoples R China|Northeastern Univ, Int Joint Res Lab Integrated Automat, Shenyang 110819, Liaoning, Peoples R China|Univ Texas Arlington, Res Inst, Ft Worth, TX 76118 USA;

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  • 正文语种 eng
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  • 关键词

    Data-driven; dual-rate; hardware-in-the-loop; lifting technology; operational optimal control (OOC); reinforcement learning (RL);

    机译：数据驱动;双速率;在环硬件;提升技术;最优操作控制（OOC）;强化学习（RL）;

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