22 June 2017
San Francesco - Via della Quarquonia 1 (Classroom 2 )
In many nonlinear control problems, the plant can be accurately described by Linear Parameter-Varying (LPV) models, with a linear dynamic input/output relationship depending on the operating conditions of the plant. Based on the LPV model of the plant, efficient methodologies are available to design LPV controllers. However, when an LPV model of the open-loop plant is derived from a set of data, several issues arise in terms of parameterization, estimation, and validation of the model before designing the controller. Moreover, the way modeling errors affect the closed-loop performance is still largely unknown in the LPV context. In this seminar, a direct data-driven (or model-free) control method is presented to design LPV controllers directly from data without deriving a model of the plant. The main idea of the approach is to use a hierarchical control architecture, where an inner LPV controller is designed directly from data to match a simple and a-priori specified closed-loop behavior. Then, an outer model predictive controller is synthesized to handle input/output constraints and to enhance the performance of the inner loop.