An optimal filter based MPC for systems with arbitrary disturbances

Haokun Wang; Zuhua Xu; Jun Zhao; Aipeng Jiang

doi:10.1016/j.cjche.2016.09.011

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An optimal filter based MPC for systems with arbitrary disturbances

Process Systems Engineering and Process Safety | Updated：2026-01-06

- An optimal filter based MPC for systems with arbitrary disturbances
- An optimal filter based MPC for systems with arbitrary disturbances
- 中国化学工程学报（英文版） 2017年25卷第5期页码：632-640
- Affiliations：
  
  1. School of Automation, Hangzhou Dianzi University,Hangzhou,China,310018
  2. National Laboratory of Industrial Control Technology, Department of Control Science and Engineering, Zhejiang University,Hangzhou,China,310027
- Author bio：
- Funds：
- DOI：10.1016/j.cjche.2016.09.011
  中图分类号：
- 纸质出版：2017
- Accepted：
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Haokun Wang, Zuhua Xu, Jun Zhao, 等. An optimal filter based MPC for systems with arbitrary disturbances[J]. 中国化学工程学报（英文版）, 2017,25(5):632-640.

Haokun Wang, Zuhua Xu, Jun Zhao, Aipeng Jiang. An optimal filter based MPC for systems with arbitrary disturbances[J]. , 2017, 25(5): 632-640.
Haokun Wang, Zuhua Xu, Jun Zhao, 等. An optimal filter based MPC for systems with arbitrary disturbances[J]. 中国化学工程学报（英文版）, 2017,25(5):632-640. DOI： 10.1016/j.cjche.2016.09.011.

Haokun Wang, Zuhua Xu, Jun Zhao, Aipeng Jiang. An optimal filter based MPC for systems with arbitrary disturbances[J]. , 2017, 25(5): 632-640. DOI： 10.1016/j.cjche.2016.09.011.

摘要

Abstract

In this study

a linear model predictive control (MPC) approach with optimal filters is proposed for handling unmeasured disturbances with arbitrary statistics. Two types of optimal filters are introduced into the framework of MPC to relax the assumption of integrated white noise model in existing approaches. The introduced filters are globally optimal for linear systems with unmeasured disturbances that have unknown statistics. This enables the proposed MPC to better handle disturbances without access to disturbance statistics. As a result

the effort required for disturbance modeling can be alleviated. The proposed MPC can achieve offset-free control in the presence of asymptotically constant unmeasured disturbances. Simulation results demonstrate that the proposed approach can provide an improved disturbance õrejection performance over conventional approaches when applied to the control of systems with unmeasured disturbances that have arbitrary statistics.

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