Data-Driven Control Design by Prediction Error Identification for Multivariable Systems

Abstract

This paper deals with data-driven control design in a model reference framework for multivariable systems. Based on a single batch of input–output data collected from the process, a fixed structure controller is estimated without using a process model, by embedding the control design problem in the prediction error identification of an optimal controller. This is an extension of optimal controller identification (OCI) for multivariable systems. Even though the multiple-input multiple-output (MIMO) formulation is extended from its single-input single-output version in a natural way, the solution of the optimization problem is rather complex due to the special structure the inverse of the controller assumes in its MIMO version. Comparisons between the OCI and the virtual reference feedback tuning—a well-known data-driven control method—are provided, showing the efficiency of the OCI controller estimate. We also explore the case where the batch of design data is collected in closed loop. Simulated and experimental results show the efficiency of the proposed methodology.