Yimeng Li;Jun Yang;Junxiao Wang;Zihao Chen;Xinming Wang;Jinhao Liu;Shihua Li
{"title":"摄动电力传动的保稳定有限控制集模型预测控制","authors":"Yimeng Li;Jun Yang;Junxiao Wang;Zihao Chen;Xinming Wang;Jinhao Liu;Shihua Li","doi":"10.1109/TII.2025.3567384","DOIUrl":null,"url":null,"abstract":"The development of rigorous theoretical tools, such as feasibility and stability analysis, for finite-control-set model predictive control (FCS-MPC) of electric drives, has lagged behind advancements in engineering practice. To address this gap, this article introduces a unified control method that integrates disturbance estimation and control Lyapunov functions (CLFs) within the FCS-MPC framework. This approach is applied to perturbed electrical drive systems, with inverter-fed permanent magnet synchronous motor systems as a primary example. First, we propose a specific class of CLFs that enables separate design of disturbance estimation, later incorporating it as a constraint in the optimization problem. Theorems and lemmas are then provided to demonstrate that using the disturbance-estimation-based control Lyapunov function constraint in an FCS-MPC setting ensures a nonempty feasible control set, guaranteeing closed-loop stability by design. Experiments conducted on a test bench validate the practicability of the proposed method. A comprehensive performance evaluation is presented under various conditions.","PeriodicalId":13301,"journal":{"name":"IEEE Transactions on Industrial Informatics","volume":"21 9","pages":"6594-6604"},"PeriodicalIF":9.9000,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Stability-Assured Finite-Control-Set Model Predictive Control for Perturbed Electrical Drives\",\"authors\":\"Yimeng Li;Jun Yang;Junxiao Wang;Zihao Chen;Xinming Wang;Jinhao Liu;Shihua Li\",\"doi\":\"10.1109/TII.2025.3567384\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The development of rigorous theoretical tools, such as feasibility and stability analysis, for finite-control-set model predictive control (FCS-MPC) of electric drives, has lagged behind advancements in engineering practice. To address this gap, this article introduces a unified control method that integrates disturbance estimation and control Lyapunov functions (CLFs) within the FCS-MPC framework. This approach is applied to perturbed electrical drive systems, with inverter-fed permanent magnet synchronous motor systems as a primary example. First, we propose a specific class of CLFs that enables separate design of disturbance estimation, later incorporating it as a constraint in the optimization problem. Theorems and lemmas are then provided to demonstrate that using the disturbance-estimation-based control Lyapunov function constraint in an FCS-MPC setting ensures a nonempty feasible control set, guaranteeing closed-loop stability by design. Experiments conducted on a test bench validate the practicability of the proposed method. A comprehensive performance evaluation is presented under various conditions.\",\"PeriodicalId\":13301,\"journal\":{\"name\":\"IEEE Transactions on Industrial Informatics\",\"volume\":\"21 9\",\"pages\":\"6594-6604\"},\"PeriodicalIF\":9.9000,\"publicationDate\":\"2025-06-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Industrial Informatics\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/11020790/\",\"RegionNum\":1,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"AUTOMATION & CONTROL SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Industrial Informatics","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/11020790/","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
Stability-Assured Finite-Control-Set Model Predictive Control for Perturbed Electrical Drives
The development of rigorous theoretical tools, such as feasibility and stability analysis, for finite-control-set model predictive control (FCS-MPC) of electric drives, has lagged behind advancements in engineering practice. To address this gap, this article introduces a unified control method that integrates disturbance estimation and control Lyapunov functions (CLFs) within the FCS-MPC framework. This approach is applied to perturbed electrical drive systems, with inverter-fed permanent magnet synchronous motor systems as a primary example. First, we propose a specific class of CLFs that enables separate design of disturbance estimation, later incorporating it as a constraint in the optimization problem. Theorems and lemmas are then provided to demonstrate that using the disturbance-estimation-based control Lyapunov function constraint in an FCS-MPC setting ensures a nonempty feasible control set, guaranteeing closed-loop stability by design. Experiments conducted on a test bench validate the practicability of the proposed method. A comprehensive performance evaluation is presented under various conditions.
期刊介绍:
The IEEE Transactions on Industrial Informatics is a multidisciplinary journal dedicated to publishing technical papers that connect theory with practical applications of informatics in industrial settings. It focuses on the utilization of information in intelligent, distributed, and agile industrial automation and control systems. The scope includes topics such as knowledge-based and AI-enhanced automation, intelligent computer control systems, flexible and collaborative manufacturing, industrial informatics in software-defined vehicles and robotics, computer vision, industrial cyber-physical and industrial IoT systems, real-time and networked embedded systems, security in industrial processes, industrial communications, systems interoperability, and human-machine interaction.