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摘要: 本文针对PLC和虚拟PLC的PID难以优化整定的难题, 将建模、控制、优化和深度学习与强化学习相结合, 提出了无模型PID在线自优化整定算法. 将工业云与边缘计算、软件定义实时与可靠保障机制的双通道通信与所提出的PID整定算法相结合, 提出了云端协同的软件定义智能控制系统. 云为基于云服务器的智能控制软件开发平台; 端为基于工业服务器的智能控制软件. 智能控制软件由虚拟PLC PID、PID预优化整定、控制过程数字孪生、在线自优化整定、自适应切换机制组成. 采用研制的软件定义智能控制系统研究实验平台, 开展了所提出的控制系统与国外先进PLC和工业PC的无模型整定软件PID控制系统的仿真与物理对比实验. 实验结果表明本文的软件定义智能控制系统可进行控制器参数自优化整定, 控制性能显著优于国外无模型整定软件的PID控制系统.
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关键词:
- 深度学习 /
- 强化学习 /
- 数字孪生 /
- PLC(可编程逻辑控制器) /
- 软件定义智能控制
Abstract: To address the challenge of achieving optimal PID tuning in both physical PLCs and virtual PLCs, a model-free PID online self-optimizing tuning algorithm is proposed by integrating modeling, control, optimization with deep learning and reinforcement learning. A cloud-edge collaborative software-defined intelligent control system is developed by combining the industrial cloud and edge computing as well as the proposed PID tuning algorithm and a software-defined dual-channel communication architecture based on real-time and reliability assurance mechanisms. This system consists of an intelligent control software development platform based on cloud servers and configurable intelligent control software based on industrial servers. The intelligent control software includes virtual PLC PID, pre-optimization PID tuning, digital twin of the control process, online self-optimizing tuning and an adaptive switching mechanism. Simulation and physical experiments on the developed research experimental platform are conducted between the proposed control system and model-free PID tuning control systems on advanced PLCs and industrial PCs. The experimental results indicate that the proposed software-defined intelligent control system is capable of self-optimizing controller parameter, and its control performance significantly outperforms that of advanced model-free tuning PID control systems. -
图 1 无模型PID在线自优化整定算法结构
Fig. 1 Model-free PID online self-optimizing tuning algorithm structure
图 4 基于强化学习的PID整定算法结构
Fig. 4 Structure of PID tuning algorithm based on reinforcement learning
图 7 智能控制软件模块之间数据交互图
Fig. 7 Data interaction diagram between intelligent control software modules
图 8 软件定义的智能控制系统研究实验平台
Fig. 8 Research and experimental platform for software-defined intelligent control system
图 9 西门子无模型整定PID控制输出与输入曲线
Fig. 9 Input and output curves of PID control tuned by Siemens model-free tuning software
图 10 倍福无模型整定PID控制输出与输入曲线
Fig. 10 Input and output curves of PID control tuned by Beckhoff model-free tuning software
图 11 本文无模型整定PID控制输出与输入曲线
Fig. 11 Input and output curves of PID control tuned by this article model-free tuning software
图 12 西门子无模型整定PID控制输出与输入曲线
Fig. 12 Input and output curves of PID control tuned by Siemens model-free tuning software
图 13 倍福无模型整定PID控制输出与输入曲线
Fig. 13 Input and output curves of PID control tuned by Beckhoff model-free tuning software
图 14 本文无模型整定PID控制输出与输入曲线
Fig. 14 Input and output curves of PID control tuned by this article model-free tuning software
表 1 本文、西门子、倍福控制性能指标对比
Table 1 Comparison of control performance indicators between Siemens, Beckhoff and this article
性能指标 $ 0 \leq k< 2590 $ $ 2590 \leq k< 4600 $ $ 4600 \leq k \leq 5000 $ $ J_e $ $ \bar{J}_e $ $ \bar{J}_u $ $ J_e $ $ \bar{J}_e $ $ \bar{J}_u $ $ J_e $ $ \bar{J}_e $ $ \bar{J}_u $ 西门子 0.026 0.041 0.000 0.112 0.067 0.013 0.132 0.135 0.018 倍福 0.019 0.039 0.000 0.063 0.053 0.017 3.181 0.848 0.620 本文 0.018 0.031 0.000 0.060 0.051 0.012 0.036 0.052 0.000 
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表 2 本文、西门子、倍福控制性能指标对比
Table 2 Comparison of control performance indicators between Siemens, Beckhoff and this article
性能指标 $ 0 \leq k< 290 $ $ 290 \leq k \leq 600 $ $ J_e $ $ \bar{J}_e $ $ \bar{J}_u $ $ J_e $ $ \bar{J}_e $ $ \bar{J}_u $ 西门子 215.18 0.379 0.17 56.45 0.177 0.04 倍福 1915.88 0.651 0.26 2118.48 0.765 0.25 本文 163.21 0.207 0.00 28.13 0.064 0.00
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