R. Romano, Marcelo Lima, P. Santos, T. Perdicoulis
Aerospace structures are often submitted to air-load tests to check possible unstable structural modes that lead to failure. These tests induce structural oscillations stimulating the system with different wind velocities, known as flutter test. An alternative is assessing critical operating regimes through simulations. Although cheaper, modelbased flutter tests rely on an accurate simulation model of the structure under investigation. This chapter addresses the data-driven flutter modeling using state-space linear parameter varying (LPV) models. The estimation algorithm employs support vector machines to represent the functional dependence between the model coefficients and the scheduling signal, which values can be used to account for different operating conditions. Besides versatile, that model structure allows the formalization of the estimation task as a linear least-squares problem. The proposed method also exploits the ensemble concept, which consists of estimating multiple models from different data partitions. These models are merged into a final one, according to their ability to reproduce a validation data segment. A case study based on real data shows that this approach resulted in a more accurate model for the available data. The local stability of the identified LPV model is also investigated to provide insights about critical operating ranges as a function of the magnitude of the input and output signals.
{"title":"Identification of a quasi-LPV model for wing-flutter analysis using machine-learning techniques","authors":"R. Romano, Marcelo Lima, P. Santos, T. Perdicoulis","doi":"10.1049/pbce123e_ch3","DOIUrl":"https://doi.org/10.1049/pbce123e_ch3","url":null,"abstract":"Aerospace structures are often submitted to air-load tests to check possible unstable structural modes that lead to failure. These tests induce structural oscillations stimulating the system with different wind velocities, known as flutter test. An alternative is assessing critical operating regimes through simulations. Although cheaper, modelbased flutter tests rely on an accurate simulation model of the structure under investigation. This chapter addresses the data-driven flutter modeling using state-space linear parameter varying (LPV) models. The estimation algorithm employs support vector machines to represent the functional dependence between the model coefficients and the scheduling signal, which values can be used to account for different operating conditions. Besides versatile, that model structure allows the formalization of the estimation task as a linear least-squares problem. The proposed method also exploits the ensemble concept, which consists of estimating multiple models from different data partitions. These models are merged into a final one, according to their ability to reproduce a validation data segment. A case study based on real data shows that this approach resulted in a more accurate model for the available data. The local stability of the identified LPV model is also investigated to provide insights about critical operating ranges as a function of the magnitude of the input and output signals.","PeriodicalId":173898,"journal":{"name":"Data-Driven Modeling, Filtering and Control: Methods and applications","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116221140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
D. Piga, S. Formentin, R. Tóth, A. Bemporad, S. Savaresi
Modeling is recognized to be one of the toughest and most time-consuming tasks in modern nonlinear control engineering applications. Linear parameter-varying (LPV) models deal with such complex problems in an effective way, by exploiting wellestablished tools for linear systems while, at the same time, being able to accurately describe highly nonlinear and time-varying plants. When LPV models are derived from experimental data, it is difficult to estimate a priori how modeling errors will affect the closed-loop performance. In this work, a method is proposed to directly map data onto LPV controllers. Specifically, a hierarchical structure is proposed both to maximize the system performance and to handle signal constraints. The effectiveness of the approach is illustrated via suitable simulation tests.
{"title":"A hierarchical approach to data-driven LPV control design of constrained systems","authors":"D. Piga, S. Formentin, R. Tóth, A. Bemporad, S. Savaresi","doi":"10.1049/pbce123e_ch11","DOIUrl":"https://doi.org/10.1049/pbce123e_ch11","url":null,"abstract":"Modeling is recognized to be one of the toughest and most time-consuming tasks in modern nonlinear control engineering applications. Linear parameter-varying (LPV) models deal with such complex problems in an effective way, by exploiting wellestablished tools for linear systems while, at the same time, being able to accurately describe highly nonlinear and time-varying plants. When LPV models are derived from experimental data, it is difficult to estimate a priori how modeling errors will affect the closed-loop performance. In this work, a method is proposed to directly map data onto LPV controllers. Specifically, a hierarchical structure is proposed both to maximize the system performance and to handle signal constraints. The effectiveness of the approach is illustrated via suitable simulation tests.","PeriodicalId":173898,"journal":{"name":"Data-Driven Modeling, Filtering and Control: Methods and applications","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133143302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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