{"title":"用于双环车辆控制设计的优化工具:分析和偏航率跟踪案例研究","authors":"Federico Dettù , Giacomo Delcaro , Simone Formentin , Stefano Varisco , Sergio Matteo Savaresi","doi":"10.1016/j.ejcon.2024.100998","DOIUrl":null,"url":null,"abstract":"<div><p>Given the urgent need to simplify the end-of-line tuning of complex vehicle dynamics controllers, the Twin-in-the-Loop Control (TiL-C) approach was recently proposed in the automotive field. In TiL-C, a digital twin is run in real time on-board the vehicle to compute a nominal control action; an additional controller is used to compensate for the mismatch between the simulator and the actual vehicle. As the digital twin is assumed to be the best replica available of the real plant, the key issue in TiL-C becomes the tuning of the compensator, which must be performed relying on data only. In this paper, we investigate the use of different black-box optimization techniques for the calibration of the compensator. More specifically, we compare the initially proposed Bayesian Optimization (BO) approach with Virtual Reference Feedback Tuning (VRFT), a one-shot direct data-driven design method, and with Set Membership Global Optimization (SMGO), a recently proposed black-box optimization method. The analysis will be carried out within a professional multibody simulation environment on a novel TiL-C application case study – the yaw-rate tracking problem – to further prove the TiL-C effectiveness on a challenging problem. Simulations will show that the VRFT approach is capable of providing a well-tuned controller after a single iteration, while 10 to 15 iterations are necessary for refining it with global optimizers. Also, SMGO is shown to reduce the computational effort required by BO significantly.</p></div>","PeriodicalId":50489,"journal":{"name":"European Journal of Control","volume":null,"pages":null},"PeriodicalIF":2.5000,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Optimization tools for Twin-in-the-Loop vehicle control design: analysis and yaw-rate tracking case study\",\"authors\":\"Federico Dettù , Giacomo Delcaro , Simone Formentin , Stefano Varisco , Sergio Matteo Savaresi\",\"doi\":\"10.1016/j.ejcon.2024.100998\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Given the urgent need to simplify the end-of-line tuning of complex vehicle dynamics controllers, the Twin-in-the-Loop Control (TiL-C) approach was recently proposed in the automotive field. In TiL-C, a digital twin is run in real time on-board the vehicle to compute a nominal control action; an additional controller is used to compensate for the mismatch between the simulator and the actual vehicle. As the digital twin is assumed to be the best replica available of the real plant, the key issue in TiL-C becomes the tuning of the compensator, which must be performed relying on data only. In this paper, we investigate the use of different black-box optimization techniques for the calibration of the compensator. More specifically, we compare the initially proposed Bayesian Optimization (BO) approach with Virtual Reference Feedback Tuning (VRFT), a one-shot direct data-driven design method, and with Set Membership Global Optimization (SMGO), a recently proposed black-box optimization method. The analysis will be carried out within a professional multibody simulation environment on a novel TiL-C application case study – the yaw-rate tracking problem – to further prove the TiL-C effectiveness on a challenging problem. Simulations will show that the VRFT approach is capable of providing a well-tuned controller after a single iteration, while 10 to 15 iterations are necessary for refining it with global optimizers. Also, SMGO is shown to reduce the computational effort required by BO significantly.</p></div>\",\"PeriodicalId\":50489,\"journal\":{\"name\":\"European Journal of Control\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.5000,\"publicationDate\":\"2024-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"European Journal of Control\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S094735802400058X\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"AUTOMATION & CONTROL SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"European Journal of Control","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S094735802400058X","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
Optimization tools for Twin-in-the-Loop vehicle control design: analysis and yaw-rate tracking case study
Given the urgent need to simplify the end-of-line tuning of complex vehicle dynamics controllers, the Twin-in-the-Loop Control (TiL-C) approach was recently proposed in the automotive field. In TiL-C, a digital twin is run in real time on-board the vehicle to compute a nominal control action; an additional controller is used to compensate for the mismatch between the simulator and the actual vehicle. As the digital twin is assumed to be the best replica available of the real plant, the key issue in TiL-C becomes the tuning of the compensator, which must be performed relying on data only. In this paper, we investigate the use of different black-box optimization techniques for the calibration of the compensator. More specifically, we compare the initially proposed Bayesian Optimization (BO) approach with Virtual Reference Feedback Tuning (VRFT), a one-shot direct data-driven design method, and with Set Membership Global Optimization (SMGO), a recently proposed black-box optimization method. The analysis will be carried out within a professional multibody simulation environment on a novel TiL-C application case study – the yaw-rate tracking problem – to further prove the TiL-C effectiveness on a challenging problem. Simulations will show that the VRFT approach is capable of providing a well-tuned controller after a single iteration, while 10 to 15 iterations are necessary for refining it with global optimizers. Also, SMGO is shown to reduce the computational effort required by BO significantly.
期刊介绍:
The European Control Association (EUCA) has among its objectives to promote the development of the discipline. Apart from the European Control Conferences, the European Journal of Control is the Association''s main channel for the dissemination of important contributions in the field.
The aim of the Journal is to publish high quality papers on the theory and practice of control and systems engineering.
The scope of the Journal will be wide and cover all aspects of the discipline including methodologies, techniques and applications.
Research in control and systems engineering is necessary to develop new concepts and tools which enhance our understanding and improve our ability to design and implement high performance control systems. Submitted papers should stress the practical motivations and relevance of their results.
The design and implementation of a successful control system requires the use of a range of techniques:
Modelling
Robustness Analysis
Identification
Optimization
Control Law Design
Numerical analysis
Fault Detection, and so on.