Xuechun Hu , Yu Xia , Zsófia Lendek , Jinde Cao , Radu-Emil Precup
{"title":"阶跃负载下永磁同步电机动态预定性能模糊神经反步控制","authors":"Xuechun Hu , Yu Xia , Zsófia Lendek , Jinde Cao , Radu-Emil Precup","doi":"10.1016/j.neunet.2025.107627","DOIUrl":null,"url":null,"abstract":"<div><div>In order to meet the performance requirements of permanent magnet synchronous motor (PMSM) systems with time-varying model parameters and input constraints under step load, this paper proposes a dynamic prescribed performance fuzzy-neural backstepping control approach. Firstly, a novel finite-time asymmetric dynamic prescribed performance function (FADPPF) is proposed to tackle the issues of exceeding predefined error, control singularity, and system instability that arise in the traditional prescribed performance function under load changes. To address model accuracy degradation and control quality deterioration caused by nonlinear time-varying parameters and input constraints in the PMSM system, a backstepping controller is designed by combining the speed function (SF), fuzzy neural network (FNN), and the proposed FADPPF. The FNN approximates nonlinear uncertain functions in the system model; the SF, as an error amplification mechanism, works together with FADPPF to ensure the transient and steady-state performance of the system. The stability of the devised control strategy is proved using Lyapunov analysis. Finally, simulation results demonstrate the dynamic self-adjusting ability and effectiveness of FADPPF under step load. In addition, the feasibility and superiority of the proposed control scheme are validated.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"190 ","pages":"Article 107627"},"PeriodicalIF":6.0000,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A novel dynamic prescribed performance fuzzy-neural backstepping control for PMSM under step load\",\"authors\":\"Xuechun Hu , Yu Xia , Zsófia Lendek , Jinde Cao , Radu-Emil Precup\",\"doi\":\"10.1016/j.neunet.2025.107627\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>In order to meet the performance requirements of permanent magnet synchronous motor (PMSM) systems with time-varying model parameters and input constraints under step load, this paper proposes a dynamic prescribed performance fuzzy-neural backstepping control approach. Firstly, a novel finite-time asymmetric dynamic prescribed performance function (FADPPF) is proposed to tackle the issues of exceeding predefined error, control singularity, and system instability that arise in the traditional prescribed performance function under load changes. To address model accuracy degradation and control quality deterioration caused by nonlinear time-varying parameters and input constraints in the PMSM system, a backstepping controller is designed by combining the speed function (SF), fuzzy neural network (FNN), and the proposed FADPPF. The FNN approximates nonlinear uncertain functions in the system model; the SF, as an error amplification mechanism, works together with FADPPF to ensure the transient and steady-state performance of the system. The stability of the devised control strategy is proved using Lyapunov analysis. Finally, simulation results demonstrate the dynamic self-adjusting ability and effectiveness of FADPPF under step load. In addition, the feasibility and superiority of the proposed control scheme are validated.</div></div>\",\"PeriodicalId\":49763,\"journal\":{\"name\":\"Neural Networks\",\"volume\":\"190 \",\"pages\":\"Article 107627\"},\"PeriodicalIF\":6.0000,\"publicationDate\":\"2025-05-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Neural Networks\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0893608025005076\",\"RegionNum\":1,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Neural Networks","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0893608025005076","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
A novel dynamic prescribed performance fuzzy-neural backstepping control for PMSM under step load
In order to meet the performance requirements of permanent magnet synchronous motor (PMSM) systems with time-varying model parameters and input constraints under step load, this paper proposes a dynamic prescribed performance fuzzy-neural backstepping control approach. Firstly, a novel finite-time asymmetric dynamic prescribed performance function (FADPPF) is proposed to tackle the issues of exceeding predefined error, control singularity, and system instability that arise in the traditional prescribed performance function under load changes. To address model accuracy degradation and control quality deterioration caused by nonlinear time-varying parameters and input constraints in the PMSM system, a backstepping controller is designed by combining the speed function (SF), fuzzy neural network (FNN), and the proposed FADPPF. The FNN approximates nonlinear uncertain functions in the system model; the SF, as an error amplification mechanism, works together with FADPPF to ensure the transient and steady-state performance of the system. The stability of the devised control strategy is proved using Lyapunov analysis. Finally, simulation results demonstrate the dynamic self-adjusting ability and effectiveness of FADPPF under step load. In addition, the feasibility and superiority of the proposed control scheme are validated.
期刊介绍:
Neural Networks is a platform that aims to foster an international community of scholars and practitioners interested in neural networks, deep learning, and other approaches to artificial intelligence and machine learning. Our journal invites submissions covering various aspects of neural networks research, from computational neuroscience and cognitive modeling to mathematical analyses and engineering applications. By providing a forum for interdisciplinary discussions between biology and technology, we aim to encourage the development of biologically-inspired artificial intelligence.