Direct Data-Driven Design for a Sparse Feedback Controller Based on VRFT and LASSO regression

Abstract

Direct data-driven controller design has attracted considerable attention because of simple design procedure. This paper presents a direct model-referenced data-driven design for obtaining sparse controller to prevent over learning from single-experiment data. In conventional methods, controller parameters are obtained for controller with order defined by user. In case where there is little plant information, it is difficult to define controller order to achieve model-matching. Also, if high order of the controller is simply set, over learning may occur. Thus, the sparse controller design method is proposed. The proposed method is based on VRFT (virtual reference feedback tuning) and LASSO (least absolute shrinkage and selection operator) regression. A simulation is performed to verify the proposed method. The results show that the sparse controller can be obtained for the controller with high order.