{"title":"基于球极坐标的编码解码量化学习控制","authors":"Niu Huo;Dong Shen;Daniel W. C. Ho","doi":"10.1109/TCYB.2024.3496794","DOIUrl":null,"url":null,"abstract":"This study investigates the performance of discrete-time systems under quantized iterative learning control. An encoding–decoding mechanism is combined with a spherical polar coordinate-based quantizer to process the signals transmitted through a control network, which introduces a quantization operation to the encoding process. A scenario involving encoding and decoding of the system output is explored before discussing the general scenario involving encoding and decoding of both the system output and control input. Unlike existing schemes, the two scenarios require no additional scaling parameter in the encoder and decoder. The radius of the support sphere is designed to vary over the iterations, and the learning control scheme is based on the output of the decoder. The results indicate that the control method enables error-free tracking performance of a system. The theoretical conclusions are verified in tests of a permanent magnet synchronous motor.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"55 2","pages":"812-825"},"PeriodicalIF":9.4000,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Encoding–Decoding-Based Quantized Learning Control Using Spherical Polar Coordinates\",\"authors\":\"Niu Huo;Dong Shen;Daniel W. C. Ho\",\"doi\":\"10.1109/TCYB.2024.3496794\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This study investigates the performance of discrete-time systems under quantized iterative learning control. An encoding–decoding mechanism is combined with a spherical polar coordinate-based quantizer to process the signals transmitted through a control network, which introduces a quantization operation to the encoding process. A scenario involving encoding and decoding of the system output is explored before discussing the general scenario involving encoding and decoding of both the system output and control input. Unlike existing schemes, the two scenarios require no additional scaling parameter in the encoder and decoder. The radius of the support sphere is designed to vary over the iterations, and the learning control scheme is based on the output of the decoder. The results indicate that the control method enables error-free tracking performance of a system. The theoretical conclusions are verified in tests of a permanent magnet synchronous motor.\",\"PeriodicalId\":13112,\"journal\":{\"name\":\"IEEE Transactions on Cybernetics\",\"volume\":\"55 2\",\"pages\":\"812-825\"},\"PeriodicalIF\":9.4000,\"publicationDate\":\"2024-11-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Cybernetics\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10769086/\",\"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 Cybernetics","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10769086/","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}
Encoding–Decoding-Based Quantized Learning Control Using Spherical Polar Coordinates
This study investigates the performance of discrete-time systems under quantized iterative learning control. An encoding–decoding mechanism is combined with a spherical polar coordinate-based quantizer to process the signals transmitted through a control network, which introduces a quantization operation to the encoding process. A scenario involving encoding and decoding of the system output is explored before discussing the general scenario involving encoding and decoding of both the system output and control input. Unlike existing schemes, the two scenarios require no additional scaling parameter in the encoder and decoder. The radius of the support sphere is designed to vary over the iterations, and the learning control scheme is based on the output of the decoder. The results indicate that the control method enables error-free tracking performance of a system. The theoretical conclusions are verified in tests of a permanent magnet synchronous motor.
期刊介绍:
The scope of the IEEE Transactions on Cybernetics includes computational approaches to the field of cybernetics. Specifically, the transactions welcomes papers on communication and control across machines or machine, human, and organizations. The scope includes such areas as computational intelligence, computer vision, neural networks, genetic algorithms, machine learning, fuzzy systems, cognitive systems, decision making, and robotics, to the extent that they contribute to the theme of cybernetics or demonstrate an application of cybernetics principles.