Multirate MVC Design and Control Performance Assessment: a Data Driven Subspace Approach*

This paper discusses minimum variance control (MVC) design and control performance assessment based on the MVC-benchmark for multirate systems. In particular, a dual-rate system with a fast control updating rate and a slow output sampling rate is considered. The lifting technique is used as an alternative way to derive a subspace equation, which is used to design the multirate MVC law, and to estimate the multirate MVC-benchmark variance. The multirate optimal controller design is data-driven since it does not involve solving Diophantine or Riccati equations, and it does not even involve identification of a parametric model. The presented MVC-benchmark variance estimation algorithm is model-free because no prior knowledge, namely, transfer function matrices, Markov parameters or interactor matrices, are needed. The proposed methods are illustrated through a simulation example.