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摘要: 本文在分析智能制造对PID整定的新需求及PID整定面临的挑战难题的基础上, 将自动化的建模、控制与优化和人工智能的深度学习与强化学习深度融合与协同, 提出了自适应与自主的PID整定的智能优化方法, 包括端边云协同的PID控制过程数字孪生模型和强化学习与数字孪生模型相结合的PID整定算法. 将工业互联网的端边云协同技术与PLC控制系统相结合, 研制了PID整定智能系统, 并在重大耗能设备 — 电熔镁炉成功应用. 该系统安全、可靠与优化运行, 取得显著的节能减排效果. 最后, 提出了控制系统智能化研究方向需要进一步深入研究的内容.Abstract: Based on the analysis of the new requirements of intelligent manufacturing for PID tuning and the challenges and difficulties faced by PID tuning, this paper proposes an adaptive and autonomous PID tuning intelligent optimization method by deeply integrates and coordinates the modeling, control and optimization in automation and deep learning and reinforcement learning in artificial intelligence. The proposed method contains the digital twin model of the PID control process based on end-edge-cloud collaboration and the PID tuning algorithm combining reinforcement learning and digital twin model. Furthermore, the PID tuning intelligent system is developed by combining the end-edge-cloud collaboration technology of Industrial Internet with the PLC control system, and has been successfully applied to the energy intensive equipment — Fused magnesium furnace. This system operates safely, reliably and optimally, achieving remarkable effects in energy conservation and emission reduction. Finally, the further research content in the intelligent research direction of control system is proposed.
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图 2 端边云协同的PID控制过程数字孪生模型结构
Fig. 2 The structure of the digital twin model of PID control process based on end-edge-cloud collaboration
图 5 PID整定智能系统功能与架构
Fig. 5 The function and architecture of PID tuning intelligent system
图 7
${\bar{v}_i}(k)$ 的自适应深度学习模型的长短周期记忆网络架构Fig. 7 The long short-term memory architecture of the adaptive deep learning model of
${\bar{v}_i}(k) $ 
图 8 电流实际值和数字孪生模型输出曲线
Fig. 8 The curves of the actual current value and the output of the digital twin model
图 9 三相电流PID控制器性能指标和整定参数迭代曲线
Fig. 9 The iterative curves of the performance index and the tunning parameters of the current PID controllers for three phases
图 10 端边云协同的电熔镁炉PID整定智能系统结构
Fig. 10 The structure for PID tuning intelligent system of fused magnesia furnace based on end-edge-cloud collaboration
图 11 采用常规PID控制算法时电极电流
$ {y_i}(k)$ 、熔化电流设定值$ {y_{sp}}(k)$ 和控制输入$ {u_i}(k)$ 的曲线Fig. 11 The curves of electrode current
${y_i}(k) $ , melting current setting${y_{sp}}(k) $ and control input${u_i}(k) $ with the conventional PID control algorithm
图 12 采用本文所提优化整定PID控制算法时电极电流
$ {y_i}(k)$ 、熔化电流设定值$ {y_{sp}}(k)$ 和控制输入$ {u_i}(k)$ 的曲线Fig. 12 The curves of electrode current
${y_i}(k) $ , melting current setting$ {y_{sp}}(k) $ and control input$ {u_i}(k) $ with the proposed algorithm表 1 数字孪生模型精度评价表
Table 1 The evaluation table of the accuracy of the digital twin model
云数字孪生模型 边数字孪生模型 MAE RMSE MAE RMSE y1(k) 429.144 535.359 466.751 651.642 y2(k) 369.998 474.175 375.679 487.996 y3(k) 341.209 450.719 363.354 451.023 
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表 2 常规PID方法和本文方法的控制性能评价表(N = 1000)
Table 2 The evaluation table of the control performance of the conventional PID method and the proposed method
MSE×10−6 IAE×10−6 ${e_1}(k)$ ${e_2}(k)$ ${e_3}(k)$ ${e_1}(k)$ ${e_2}(k)$ ${e_3}(k)$ 常规PID方法 1.2579 1.5044 1.4332 2.7705 3.7629 3.3114 本文方法 0.6732 0.7107 0.8462 1.2579 1.5044 1.4332
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