In many nonlinear control problems, the plant can be accurately described bya linear model whose operating point depends on some measurable variables,called scheduling signals. When such a linear parameter-varying (LPV) model ofthe open-loop plant needs to be derived from a set of data, several issuesarise in terms of parameterization, estimation, and validation of the modelbefore designing the controller. Moreover, the way modeling errors affect theclosed-loop performance is still largely unknown in the LPV context. In thispaper, a direct data-driven control method is proposed to design LPVcontrollers directly from data without deriving a model of the plant. The mainidea of the approach is to use a hierarchical control architecture, where theinner controller is designed to match a simple and a-priori specifiedclosed-loop behavior. Then, an outer model predictive controller is synthesizedto handle input/output constraints and to enhance the performance of the innerloop. The effectiveness of the approach is illustrated by means of a simulationand an experimental example. Practical implementation issues are alsodiscussed.
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