A multi-iteration pseudo-linear regression method and an adaptive disturbance model for MPC

Abstract

This paper proposes an MPC method that uses an adaptive disturbance model to improve the accuracy of prediction. In unmeasured disturbance model identification, a novel multi-iteration pseudo-linear regression (MIPLR) method is used which is more accurate and has faster convergence than traditional recursive identification methods. The adaptive disturbance model is used in an MPC scheme for improved performance in disturbance rejection. The method is demonstrated by the simulation of a distillation column and also tested on the real process. The test results show that the proposed MPC scheme can not only increase control performance, but also increase robustness.