{"title":"Observer-based sliding mode control of impulsive systems with unknown mismatched disturbances","authors":"Luyao You, Xiaodi Li","doi":"10.1016/j.eswa.2025.127092","DOIUrl":null,"url":null,"abstract":"<div><div>This paper studies a novel observer-based sliding mode control problem for impulsive systems involving unknown mismatched disturbances. Different from the existing sliding mode control coupling the complete information of known disturbances, our proposed observer-based sliding mode control depends on the disturbance estimation and incorporates the bound of disturbance estimation error into the switch gain design. It is shown that the proposed observer-based sliding mode control not only implicitly restrains the negative effects of unknown mismatched disturbances, but also ensures the reachability of the designed sliding surface in a finite time. Some reachability criteria of impulsive systems are established, where a potential relationship between mismatched disturbances, impulse actions, and sliding function is presented. This relationship fully estimates the effects of discrete dynamics and avoids the resulting sliding mode dynamics jumping out of the designed sliding surface at impulse instants. Following that, some linear matrix inequalities-based conditions are obtained to stabilize the resulting sliding mode dynamics. Finally, two examples are provided to verify our results, including the one focusing on the mass–spring-damper systems.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"276 ","pages":"Article 127092"},"PeriodicalIF":7.5000,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Expert Systems with Applications","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0957417425007146","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
Observer-based sliding mode control of impulsive systems with unknown mismatched disturbances
This paper studies a novel observer-based sliding mode control problem for impulsive systems involving unknown mismatched disturbances. Different from the existing sliding mode control coupling the complete information of known disturbances, our proposed observer-based sliding mode control depends on the disturbance estimation and incorporates the bound of disturbance estimation error into the switch gain design. It is shown that the proposed observer-based sliding mode control not only implicitly restrains the negative effects of unknown mismatched disturbances, but also ensures the reachability of the designed sliding surface in a finite time. Some reachability criteria of impulsive systems are established, where a potential relationship between mismatched disturbances, impulse actions, and sliding function is presented. This relationship fully estimates the effects of discrete dynamics and avoids the resulting sliding mode dynamics jumping out of the designed sliding surface at impulse instants. Following that, some linear matrix inequalities-based conditions are obtained to stabilize the resulting sliding mode dynamics. Finally, two examples are provided to verify our results, including the one focusing on the mass–spring-damper systems.
期刊介绍:
Expert Systems With Applications is an international journal dedicated to the exchange of information on expert and intelligent systems used globally in industry, government, and universities. The journal emphasizes original papers covering the design, development, testing, implementation, and management of these systems, offering practical guidelines. It spans various sectors such as finance, engineering, marketing, law, project management, information management, medicine, and more. The journal also welcomes papers on multi-agent systems, knowledge management, neural networks, knowledge discovery, data mining, and other related areas, excluding applications to military/defense systems.