Data-Driven Model Predictive Control for Polytopic Linear Parameter Varying Systems in Presence of Measurement Noise

In this paper, an extension for Data-Driven Model Predictive Control for Linear Parameter Varying systems is presented. Model-based controllers are highly dependent on model precision. On the other hand, data-driven methods are either replaced with the model or import its dynamic data into the design. Throughout this paper, direct data-driven approaches, which have gained considerable attention in recent years, are used in designing different parts of the controller, including future predictions. In addition, the stability and recursive feasibility guarantees are presented as the first novelty of this research with respect to a prior platform for data-driven approach. Furthermore, the base platform of direct DD-MPC for LPV systems is extended. The new developed form with the goal of robustness against measurement noise is defined as the next novelty of this paper. In order to check the performance of the proposed method, simulations on DC motor are applied. The results show effectiveness of the proposed approach as compared with similar approaches reported in the literature.