An optimal filter based MPC for systems with arbitrary disturbances

doi:10.1016/j.cjche.2016.09.011

›› 2017, Vol. 25 ›› Issue (5): 632-640.DOI: 10.1016/j.cjche.2016.09.011

• Process Systems Engineering and Process Safety • Previous Articles Next Articles

An optimal filter based MPC for systems with arbitrary disturbances

Haokun Wang¹, Zuhua Xu², Jun Zhao², Aipeng Jiang¹

1 School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China;
2 National Laboratory of Industrial Control Technology, Department of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China

Received:2016-06-18 Revised:2016-09-07 Online:2017-07-06 Published:2017-05-28
Supported by:
Supported by the Startup Foundation of Hangzhou Dianzi University (ZX150204302002/009),the Open Project Program of the State Key Laboratory of Industrial Control Technology (Zhejiang University),and National Natural Science Foundation of China (No.61374142,61273145,and 61273146).

An optimal filter based MPC for systems with arbitrary disturbances

Haokun Wang¹, Zuhua Xu², Jun Zhao², Aipeng Jiang¹

1 School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China;
2 National Laboratory of Industrial Control Technology, Department of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China

通讯作者: Zuhua Xu,E-mail addresses:xuzh@iipc.zju.edu.cn;Aipeng Jiang,E-mail addresses:jiangaipeng@163.com
基金资助:
Supported by the Startup Foundation of Hangzhou Dianzi University (ZX150204302002/009),the Open Project Program of the State Key Laboratory of Industrial Control Technology (Zhejiang University),and National Natural Science Foundation of China (No.61374142,61273145,and 61273146).

Abstract

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.

Key words: Model predictive control, Optimal filter, Disturbance modeling, Disturbance statistics, Unmeasured disturbances

摘要： 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.

关键词: Model predictive control, Optimal filter, Disturbance modeling, Disturbance statistics, Unmeasured disturbances

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

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An optimal filter based MPC for systems with arbitrary disturbances

An optimal filter based MPC for systems with arbitrary disturbances

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