Data-driven tuning of model-reference controllers for stable MIMO plants

Abstract

A new data-driven tuning method of a linearly parametrized controller is proposed for stable multi-input multi-output plants. A criterion that evaluates the difference between responses of the reference model and those of the feedback system is employed for tracking control. Because the criterion is nonconvex with respect to controller parameters, its second order Taylor expansion, which is convex, is used for parameter tuning. To represent the approximated criterion using plant responses instead of a plant mathematical model, it is proposed to use the responses of all entries of the plant for a test function, typically a step function. The parameter value that minimizes the approximated data-based criterion is obtained adopting the linear least-squares method. The proposed tuning method is compared with virtual reference feedback tuning and its extended methods for multi-input multi-output plants.