{"title":"An approximation-free prescribed performance controller for uncertain MIMO feedback linearizable systems","authors":"Achilles Theodorakopoulos, G. Rovithakis","doi":"10.1109/ACC.2015.7171953","DOIUrl":null,"url":null,"abstract":"In this paper, a continuous, state-feedback controller achieving preselected bounds on the transient and steady-state performance of the output tracking errors is proposed, for a class of multi-input, multi-output, nonlinear systems. Contrary to the current state-of-the-art, however, the controller proposed herein is static, i.e., it does not require the implementation of adaptive laws, and further it does not incorporate neural networks or other approximating structures. In this respect, certain control design difficulties, including the selection of the neural network size or the vast amount of neural parameters, are effectively relaxed. Simulations are performed to verify and clarify the theoretical findings.","PeriodicalId":223665,"journal":{"name":"2015 American Control Conference (ACC)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 American Control Conference (ACC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ACC.2015.7171953","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In this paper, a continuous, state-feedback controller achieving preselected bounds on the transient and steady-state performance of the output tracking errors is proposed, for a class of multi-input, multi-output, nonlinear systems. Contrary to the current state-of-the-art, however, the controller proposed herein is static, i.e., it does not require the implementation of adaptive laws, and further it does not incorporate neural networks or other approximating structures. In this respect, certain control design difficulties, including the selection of the neural network size or the vast amount of neural parameters, are effectively relaxed. Simulations are performed to verify and clarify the theoretical findings.