{"title":"具有全状态时变约束的非线性不确定机电系统自适应容错控制","authors":"大鹏 李","doi":"10.12677/dsc.2019.84025","DOIUrl":null,"url":null,"abstract":"This paper proposes an adaptive fault-tolerant control method for nonlinear electromechanical system with full-state time varying constraints. In order to ensure the control performance of the system, a fault tolerant compensation controller is constructed to eliminate the influence of the actuator loss of effectiveness and bias fault. The time-varying barrier Lyapunov functions are introduced to ensure that all the system state does not exceed the specified time-varying constraint range; especially in the case of actuator failure, the full-state constraints are not violated. Neural network as the approximator is employed to approximate unknown function in the processing system. Based on the Lyapunov analysis, it is proved that all the signals in the closed-loop system are bounded and the good tracking performance of the system is achieved. The simulation results further illustrate the effectiveness of the proposed control strategy.","PeriodicalId":68342,"journal":{"name":"动力系统与控制","volume":"1 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Adaptive Fault-Tolerant Control for Nonlinear Uncertain Electromechanical Systems with Full-State Time-Varying Constraints\",\"authors\":\"大鹏 李\",\"doi\":\"10.12677/dsc.2019.84025\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper proposes an adaptive fault-tolerant control method for nonlinear electromechanical system with full-state time varying constraints. In order to ensure the control performance of the system, a fault tolerant compensation controller is constructed to eliminate the influence of the actuator loss of effectiveness and bias fault. The time-varying barrier Lyapunov functions are introduced to ensure that all the system state does not exceed the specified time-varying constraint range; especially in the case of actuator failure, the full-state constraints are not violated. Neural network as the approximator is employed to approximate unknown function in the processing system. Based on the Lyapunov analysis, it is proved that all the signals in the closed-loop system are bounded and the good tracking performance of the system is achieved. The simulation results further illustrate the effectiveness of the proposed control strategy.\",\"PeriodicalId\":68342,\"journal\":{\"name\":\"动力系统与控制\",\"volume\":\"1 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"动力系统与控制\",\"FirstCategoryId\":\"1087\",\"ListUrlMain\":\"https://doi.org/10.12677/dsc.2019.84025\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"动力系统与控制","FirstCategoryId":"1087","ListUrlMain":"https://doi.org/10.12677/dsc.2019.84025","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Adaptive Fault-Tolerant Control for Nonlinear Uncertain Electromechanical Systems with Full-State Time-Varying Constraints
This paper proposes an adaptive fault-tolerant control method for nonlinear electromechanical system with full-state time varying constraints. In order to ensure the control performance of the system, a fault tolerant compensation controller is constructed to eliminate the influence of the actuator loss of effectiveness and bias fault. The time-varying barrier Lyapunov functions are introduced to ensure that all the system state does not exceed the specified time-varying constraint range; especially in the case of actuator failure, the full-state constraints are not violated. Neural network as the approximator is employed to approximate unknown function in the processing system. Based on the Lyapunov analysis, it is proved that all the signals in the closed-loop system are bounded and the good tracking performance of the system is achieved. The simulation results further illustrate the effectiveness of the proposed control strategy.