{"title":"Simplified Continuous-Control-Set Model Predictive Control for LCL-Equipped High-Speed PMSM With High Dynamic Performance","authors":"Dingkuan Xu;Yu Yao;Fei Peng;Yunkai Huang","doi":"10.1109/TIE.2024.3482004","DOIUrl":null,"url":null,"abstract":"In high-speed motor drives, the installation of inductance-capacitance-inductance (LCL) filter can alleviate the additional losses caused by current harmonics and mitigate the <inline-formula><tex-math>$\\boldsymbol{dv/dt}$</tex-math></inline-formula> voltage significantly. However, sophisticated current controller design is required to eliminate the resonance introduced by the filter. Numerous active damping techniques based on frequency domain analysis have been presented, which realize satisfactory resonance suppression effect but typically have limited dynamic performance. Several model predictive control (MPC) methods have been developed to substitute the existing active damping designs and improve the control performance. However, these methods always contain the high-order observer, which increases the complexity of tuning and digital implementation. This article proposes a novel continuous-control-set MPC design that simultaneously achieves high dynamic and desirable steady-state performance without the need for a high-order observer or additional active damping. Furthermore, this article provides the frequency domain analysis and parameter design method for the proposed MPC. The simulation and experimental results demonstrate the superiority of the proposed method and its suitability for implementation in digital controllers.","PeriodicalId":13402,"journal":{"name":"IEEE Transactions on Industrial Electronics","volume":"72 5","pages":"4661-4670"},"PeriodicalIF":7.2000,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Industrial Electronics","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10738197/","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
In high-speed motor drives, the installation of inductance-capacitance-inductance (LCL) filter can alleviate the additional losses caused by current harmonics and mitigate the $\boldsymbol{dv/dt}$ voltage significantly. However, sophisticated current controller design is required to eliminate the resonance introduced by the filter. Numerous active damping techniques based on frequency domain analysis have been presented, which realize satisfactory resonance suppression effect but typically have limited dynamic performance. Several model predictive control (MPC) methods have been developed to substitute the existing active damping designs and improve the control performance. However, these methods always contain the high-order observer, which increases the complexity of tuning and digital implementation. This article proposes a novel continuous-control-set MPC design that simultaneously achieves high dynamic and desirable steady-state performance without the need for a high-order observer or additional active damping. Furthermore, this article provides the frequency domain analysis and parameter design method for the proposed MPC. The simulation and experimental results demonstrate the superiority of the proposed method and its suitability for implementation in digital controllers.
期刊介绍:
Journal Name: IEEE Transactions on Industrial Electronics
Publication Frequency: Monthly
Scope:
The scope of IEEE Transactions on Industrial Electronics encompasses the following areas:
Applications of electronics, controls, and communications in industrial and manufacturing systems and processes.
Power electronics and drive control techniques.
System control and signal processing.
Fault detection and diagnosis.
Power systems.
Instrumentation, measurement, and testing.
Modeling and simulation.
Motion control.
Robotics.
Sensors and actuators.
Implementation of neural networks, fuzzy logic, and artificial intelligence in industrial systems.
Factory automation.
Communication and computer networks.