IMT Institutional Repository: No conditions. Results ordered -Date Deposited. 2024-03-29T12:18:29ZEPrintshttp://eprints.imtlucca.it/images/logowhite.pnghttp://eprints.imtlucca.it/2016-02-29T08:59:31Z2016-02-29T08:59:31Zhttp://eprints.imtlucca.it/id/eprint/3151This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/31512016-02-29T08:59:31ZDirect learning ofLPVcontrollers from dataIn many control applications, it is attractive to describe nonlinear (NL) and time-varying (TV) plants by linear parametervarying (LPV) models and design controllers based on such representations to regulate the behaviour of the system. The LPV system class offers the representation of NL and TV phenomena as a linear dynamic relationship between input and output signals, which relationship is dependent on some measurable signals, e.g., operating conditions, often called as scheduling variables. For such models, powerful control synthesis tools are available, but the way how to systematically convert available first principles models to LPV descriptions of the plant, to efficiently identify LPV models for control from data and to understand how modeling errors affect the control performance are still subject of undergoing research. Therefore, it is attractive to synthesize the controller directly from data without the need of modeling the plant and addressing the underlying difficulties. Hence, in this paper, a novel data-driven synthesis scheme is proposed in a stochastic framework to provide a practically applicable solution for synthesizing LPV controllers directly from data. Both the cases of fixed order controller tuning and controller structure learning are discussed and two different design approaches are provided. The effectiveness of the proposed methods is also illustrated by means of an academic example and a real application based simulation case study.Simone FormentinDario Pigadario.piga@imtlucca.itRoland TóthSergio M. Savaresi2015-01-12T12:41:33Z2015-01-12T12:42:48Zhttp://eprints.imtlucca.it/id/eprint/2459This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/24592015-01-12T12:41:33ZDirect data-driven control of linear parameter-varying systemsIn many control applications, nonlinear plants can be modeled as linear parameter-varying (LPV) systems, by which the dynamic behavior is assumed to be linear, but also dependent on some measurable signals, e.g., operating conditions. When a measured data set is available, LPV model identification can provide low complexity linear models that can embed the underlying nonlinear dynamic behavior of the plant. For such models, powerful control synthesis tools are available, but the way the modeling error and the conservativeness of the embedding affect the control performance is still largely unknown. Therefore, it appears to be attractive to directly synthesize the controller from data without modeling the plant. In this paper, a novel data-driven synthesis scheme is proposed to lay the basic foundations of future research on this challenging problem. The effectiveness of the proposed approach is illustrated by a numerical example.Simone FormentinDario Pigadario.piga@imtlucca.itRoland TóthSergio M. Savaresi