Kelechi U Ebirim, Andrea Lecchini-Visintini, M. Rubagotti, E. Prempain
{"title":"Offset-Free Model Predictive Control of a Twin Rotor MIMO System (Extended Abstract)","authors":"Kelechi U Ebirim, Andrea Lecchini-Visintini, M. Rubagotti, E. Prempain","doi":"10.1109/Control55989.2022.9781370","DOIUrl":null,"url":null,"abstract":"The offset-free control of a nonlinear twin rotor MIMO system (TRMS) is challenging because of its dynamic cross-couplings. Offset-free model predictive control (MPC) strategies in the literature favour the use of a disturbance model, dependent on an observer for the estimation of some states, and a cost function that penalises the output error and control increment. We propose an alternative strategy with experimental validation, using a complete dynamic TRMS model and a cost function which penalises the states and control action, and we compare this with a baseline linear quadratic regulator (LQR) approach. Simulation results show satisfactory tracking in favour of MPC as input rate constraints are tightened.","PeriodicalId":101892,"journal":{"name":"2022 UKACC 13th International Conference on Control (CONTROL)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 UKACC 13th International Conference on Control (CONTROL)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/Control55989.2022.9781370","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
The offset-free control of a nonlinear twin rotor MIMO system (TRMS) is challenging because of its dynamic cross-couplings. Offset-free model predictive control (MPC) strategies in the literature favour the use of a disturbance model, dependent on an observer for the estimation of some states, and a cost function that penalises the output error and control increment. We propose an alternative strategy with experimental validation, using a complete dynamic TRMS model and a cost function which penalises the states and control action, and we compare this with a baseline linear quadratic regulator (LQR) approach. Simulation results show satisfactory tracking in favour of MPC as input rate constraints are tightened.