Manuel Lanchares, I. Kolmanovsky, A. Girard, Denise M. Rizzo
{"title":"Reference Governors Based on Online Learning of Maximal Output Admissible Set","authors":"Manuel Lanchares, I. Kolmanovsky, A. Girard, Denise M. Rizzo","doi":"10.1115/dscc2019-8950","DOIUrl":null,"url":null,"abstract":"\n Reference governors are add-on control schemes that modify the reference commands, if it becomes necessary, in order to avoid constraint violations. To implement a reference governor, explicit knowledge of a model of the system and its constraints is typically required. In this paper, a reference governor which does not require an explicit model of the system or constraints is presented. It constructs an approximation of the maximal output admissible set, as the system operates, using online neural network learning. This approximation is used to modify the reference command in order to satisfy the constraints. The potential of the algorithm is demonstrated through simulations for an electric vehicle and an agile positioning system.","PeriodicalId":41412,"journal":{"name":"Mechatronic Systems and Control","volume":null,"pages":null},"PeriodicalIF":1.0000,"publicationDate":"2019-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Mechatronic Systems and Control","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1115/dscc2019-8950","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 4
Abstract
Reference governors are add-on control schemes that modify the reference commands, if it becomes necessary, in order to avoid constraint violations. To implement a reference governor, explicit knowledge of a model of the system and its constraints is typically required. In this paper, a reference governor which does not require an explicit model of the system or constraints is presented. It constructs an approximation of the maximal output admissible set, as the system operates, using online neural network learning. This approximation is used to modify the reference command in order to satisfy the constraints. The potential of the algorithm is demonstrated through simulations for an electric vehicle and an agile positioning system.
期刊介绍：
This international journal publishes both theoretical and application-oriented papers on various aspects of mechatronic systems, modelling, design, conventional and intelligent control, and intelligent systems. Application areas of mechatronics may include robotics, transportation, energy systems, manufacturing, sensors, actuators, and automation. Techniques of artificial intelligence may include soft computing (fuzzy logic, neural networks, genetic algorithms/evolutionary computing, probabilistic methods, etc.). Techniques may cover frequency and time domains, linear and nonlinear systems, and deterministic and stochastic processes. Hybrid techniques of mechatronics that combine conventional and intelligent methods are also included. First published in 1972, this journal originated with an emphasis on conventional control systems and computer-based applications. Subsequently, with rapid advances in the field and in view of the widespread interest and application of soft computing in control systems, this latter aspect was integrated into the journal. Now the area of mechatronics is included as the main focus. A unique feature of the journal is its pioneering role in bridging the gap between conventional systems and intelligent systems, with an equal emphasis on theory and practical applications, including system modelling, design and instrumentation. It appears four times per year.
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