Yukai Wei, Linfang Qian, Quan Zou, Longmiao Chen, Shoucheng Nie
{"title":"采用双模型不确定性补偿的输出受限混合动力筒式升降机的自适应规定性能控制","authors":"Yukai Wei, Linfang Qian, Quan Zou, Longmiao Chen, Shoucheng Nie","doi":"10.1007/s12206-024-0838-x","DOIUrl":null,"url":null,"abstract":"<p>This study concerns the position tracking control of the hybrid-powered barrel elevator (HPBE) with dual-channel model uncertainty and output constraints. To realize an outstanding control performance, a dual-extended-state-observer-based command filtered adaptive prescribed performance control (DESO-CFAPPC) strategy is presented based on the dynamic model considering various nonlinearities and model uncertainties. The conjunction of the PPC and barrier Lyapunov function in the DESO-CFAPPC not only prevents violation of output constraints of the barrel, but also prevents the complex calculation caused by the logarithmic ETF in the traditional PPC. The adaptive laws are constructed to estimate uncertain parameters. The DESO further estimates the unknown velocity, mismatched and matched model uncertainties. The obtained estimates are incorporated into the control law to enhance the tracking performance. The stability and convergence of the DESO-CFAPPC are theoretically proved and comparative experimental results indicate the effectiveness of the proposed strategy.</p>","PeriodicalId":16235,"journal":{"name":"Journal of Mechanical Science and Technology","volume":"14 1","pages":""},"PeriodicalIF":1.5000,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Adaptive prescribed performance control of output constrained hybrid-powered barrel elevator with dual-model uncertainty compensation\",\"authors\":\"Yukai Wei, Linfang Qian, Quan Zou, Longmiao Chen, Shoucheng Nie\",\"doi\":\"10.1007/s12206-024-0838-x\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>This study concerns the position tracking control of the hybrid-powered barrel elevator (HPBE) with dual-channel model uncertainty and output constraints. To realize an outstanding control performance, a dual-extended-state-observer-based command filtered adaptive prescribed performance control (DESO-CFAPPC) strategy is presented based on the dynamic model considering various nonlinearities and model uncertainties. The conjunction of the PPC and barrier Lyapunov function in the DESO-CFAPPC not only prevents violation of output constraints of the barrel, but also prevents the complex calculation caused by the logarithmic ETF in the traditional PPC. The adaptive laws are constructed to estimate uncertain parameters. The DESO further estimates the unknown velocity, mismatched and matched model uncertainties. The obtained estimates are incorporated into the control law to enhance the tracking performance. The stability and convergence of the DESO-CFAPPC are theoretically proved and comparative experimental results indicate the effectiveness of the proposed strategy.</p>\",\"PeriodicalId\":16235,\"journal\":{\"name\":\"Journal of Mechanical Science and Technology\",\"volume\":\"14 1\",\"pages\":\"\"},\"PeriodicalIF\":1.5000,\"publicationDate\":\"2024-09-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Mechanical Science and Technology\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1007/s12206-024-0838-x\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, MECHANICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Mechanical Science and Technology","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s12206-024-0838-x","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, MECHANICAL","Score":null,"Total":0}
Adaptive prescribed performance control of output constrained hybrid-powered barrel elevator with dual-model uncertainty compensation
This study concerns the position tracking control of the hybrid-powered barrel elevator (HPBE) with dual-channel model uncertainty and output constraints. To realize an outstanding control performance, a dual-extended-state-observer-based command filtered adaptive prescribed performance control (DESO-CFAPPC) strategy is presented based on the dynamic model considering various nonlinearities and model uncertainties. The conjunction of the PPC and barrier Lyapunov function in the DESO-CFAPPC not only prevents violation of output constraints of the barrel, but also prevents the complex calculation caused by the logarithmic ETF in the traditional PPC. The adaptive laws are constructed to estimate uncertain parameters. The DESO further estimates the unknown velocity, mismatched and matched model uncertainties. The obtained estimates are incorporated into the control law to enhance the tracking performance. The stability and convergence of the DESO-CFAPPC are theoretically proved and comparative experimental results indicate the effectiveness of the proposed strategy.
期刊介绍:
The aim of the Journal of Mechanical Science and Technology is to provide an international forum for the publication and dissemination of original work that contributes to the understanding of the main and related disciplines of mechanical engineering, either empirical or theoretical. The Journal covers the whole spectrum of mechanical engineering, which includes, but is not limited to, Materials and Design Engineering, Production Engineering and Fusion Technology, Dynamics, Vibration and Control, Thermal Engineering and Fluids Engineering.
Manuscripts may fall into several categories including full articles, solicited reviews or commentary, and unsolicited reviews or commentary related to the core of mechanical engineering.