自动化学报2025，Vol.51Issue(10)：2232-2244,13.DOI:10.16383/j.aas.c250274
软件定义智能控制系统
Software-defined Intelligent Control System
摘要
Abstract
To address the challenge of achieving optimal PID tuning in both physical programmable logic control-ler(PLC)and virtual PLC,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.In this system,the cloud serves as an intelligent control software develop-ment platform based on cloud servers,while the edge comprises intelligent control software deployed 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 com-parative experiments on the developed software-defined intelligent control system research experimental platform are conducted between the proposed control system and model-free PID tuning control systems on advanced for-eign PLCs and industrial PCs.The experimental results indicate that the proposed software-defined intelligent con-trol system is capable of self-optimizing controller parameter,and its control performance significantly outperforms that of advanced foreign model-free tuning PID control systems.关键词
深度学习/强化学习/数字孪生/可编程逻辑控制器/软件定义智能控制Key words
Deep learning/reinforcement learning/digital twin/programmable logic controller/software-defined in-telligent control引用本文复制引用
柴天佑,郑锐,贾瑶,黄新宇,郑秀萍,李智..软件定义智能控制系统[J].自动化学报,2025,51(10):2232-2244,13.基金项目
辽宁省辽河实验室研究项目(LLL23ZZ-05-01),辽宁省重点研发计划(2023JH26/10200011),国家自然科学基金重大项目(61991404),国家重点研发计划(2024YFB3309700),辽宁省科技重大专项(2024JH1/11700048)资助Supported by Research Program of the Liaoning Liaohe Labor-atory(LLL23ZZ-05-01),Key Research and Development Pro-gram of Liaoning Province(2023JH26/10200011),Major Project of National Natural Science Foundation of China(61991404),Na-tional Key Research and Development Program of China(2024YFB3309700),and Science and Technology Major Project of Liaoning Province(2024JH1/11700048) （LLL23ZZ-05-01）
