{"title":"切换线性系统的全局指数稳定自适应控制:一种记忆增强方法","authors":"Pritesh Patel , Sayan Basu Roy , Shubhendu Bhasin","doi":"10.1016/j.nahs.2025.101619","DOIUrl":null,"url":null,"abstract":"<div><div>This paper introduces a switched model reference adaptive control (S-MRAC) architecture for uncertain switched multi-input multi-output (MIMO) linear time-invariant (LTI) systems with a switched reference model. One distinctive aspect of the suggested method is the use of memory to augment the parameter estimator, leading to parameter learning even during inactive periods of the subsystems. Together with an intermittently initial excitation (IIE) condition, the memory augmentation-based approach guarantees exponential stability of the tracking and parameter estimation error systems. An online parameter estimator with a dual-layer low-pass filter and a bank of memory filters is at the heart of the proposed architecture. The addition of the <span><math><mrow><mi>σ</mi><mo>−</mo></mrow></math></span> modification term in adaptive law facilitates the computation of a unified expression of dwell time that is valid for both excitation and non-excitation scenarios. Further, the dwell time expression is tunable and thus, allows for fast switching. Simulation results are showcased to confirm the efficacy of the suggested outcome.</div></div>","PeriodicalId":49011,"journal":{"name":"Nonlinear Analysis-Hybrid Systems","volume":"58 ","pages":"Article 101619"},"PeriodicalIF":3.7000,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Globally exponentially stable adaptive control of switched linear systems: A memory augmented approach\",\"authors\":\"Pritesh Patel , Sayan Basu Roy , Shubhendu Bhasin\",\"doi\":\"10.1016/j.nahs.2025.101619\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>This paper introduces a switched model reference adaptive control (S-MRAC) architecture for uncertain switched multi-input multi-output (MIMO) linear time-invariant (LTI) systems with a switched reference model. One distinctive aspect of the suggested method is the use of memory to augment the parameter estimator, leading to parameter learning even during inactive periods of the subsystems. Together with an intermittently initial excitation (IIE) condition, the memory augmentation-based approach guarantees exponential stability of the tracking and parameter estimation error systems. An online parameter estimator with a dual-layer low-pass filter and a bank of memory filters is at the heart of the proposed architecture. The addition of the <span><math><mrow><mi>σ</mi><mo>−</mo></mrow></math></span> modification term in adaptive law facilitates the computation of a unified expression of dwell time that is valid for both excitation and non-excitation scenarios. Further, the dwell time expression is tunable and thus, allows for fast switching. Simulation results are showcased to confirm the efficacy of the suggested outcome.</div></div>\",\"PeriodicalId\":49011,\"journal\":{\"name\":\"Nonlinear Analysis-Hybrid Systems\",\"volume\":\"58 \",\"pages\":\"Article 101619\"},\"PeriodicalIF\":3.7000,\"publicationDate\":\"2025-07-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Nonlinear Analysis-Hybrid Systems\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1751570X25000457\",\"RegionNum\":2,\"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":"Nonlinear Analysis-Hybrid Systems","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1751570X25000457","RegionNum":2,"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}
Globally exponentially stable adaptive control of switched linear systems: A memory augmented approach
This paper introduces a switched model reference adaptive control (S-MRAC) architecture for uncertain switched multi-input multi-output (MIMO) linear time-invariant (LTI) systems with a switched reference model. One distinctive aspect of the suggested method is the use of memory to augment the parameter estimator, leading to parameter learning even during inactive periods of the subsystems. Together with an intermittently initial excitation (IIE) condition, the memory augmentation-based approach guarantees exponential stability of the tracking and parameter estimation error systems. An online parameter estimator with a dual-layer low-pass filter and a bank of memory filters is at the heart of the proposed architecture. The addition of the modification term in adaptive law facilitates the computation of a unified expression of dwell time that is valid for both excitation and non-excitation scenarios. Further, the dwell time expression is tunable and thus, allows for fast switching. Simulation results are showcased to confirm the efficacy of the suggested outcome.
期刊介绍:
Nonlinear Analysis: Hybrid Systems welcomes all important research and expository papers in any discipline. Papers that are principally concerned with the theory of hybrid systems should contain significant results indicating relevant applications. Papers that emphasize applications should consist of important real world models and illuminating techniques. Papers that interrelate various aspects of hybrid systems will be most welcome.