Experimental Validation for Artificial Data Driven Tracking Control for Enhanced Three Phase Grid Co

In model-based control design, one often has to describe the plant by a linear model. Deriving such a model poses issues of parameterization, estimation, and validation of the model before designing the controller. In this paper, a direct data-driven control method is proposed for designing controllers that can handle constraints without deriving a model of the plant and directly from data . A hierarchical control architecture is used, in which an inner linear time-invariant or linear parameter-varying controller is first designed to match a simple and a priori specified closed-loop model. Then, an outer model predictive controller is synthesized to handle input/output constraints and to enhance the performance of the inner loop. The effectiveness of the approach is illustrated by means of a simulation and an experimental example. Practical implementation issues are also discussed.