Data-Driven Two-Dimensional LQG Benchmark Based Performance Assessment for Batch Processes under ILC ?

摘要

A novel data-driven control performance assessment (CPA) method is proposed for batch processes controlled by iterative learning control (ILC) based on two-dimensional linear quadratic Gaussian (LQG) benchmark. Previous studies on CPA for ILC are based on an assumption that the model of the controlled batch process is known, whereas this study proposes a model-free CPA method. Based on the two-dimensional system theory, the closed-loop batch process under ILC can be converted into a two-dimensional Roesser model. This study proposes a novel closed-loop two-dimensional subspace identification method for the converted parameters unknown two-dimensional Roesser model. Using the identified model, the two-dimensional LQG tradeoff performance assessment surface can be obtained. The proposed method is verified by performing some simulations.