An Enhanced Method to Assess MPC Performance Based on Multi-Step Slow Feature Analysis

Linyuan Shang, Yanjiang Wang, Xiaogang Deng, Yuping Cao, Ping Wang and Yuhong Wang
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Linyuan Shang: College of Information and Control Engineering, China University of Petroleum (East China), Qingdao 266580, Shandong, China
Yanjiang Wang: College of Information and Control Engineering, China University of Petroleum (East China), Qingdao 266580, Shandong, China
Xiaogang Deng: College of Information and Control Engineering, China University of Petroleum (East China), Qingdao 266580, Shandong, China
Yuping Cao: College of Information and Control Engineering, China University of Petroleum (East China), Qingdao 266580, Shandong, China
Ping Wang: College of Information and Control Engineering, China University of Petroleum (East China), Qingdao 266580, Shandong, China
Yuhong Wang: College of Information and Control Engineering, China University of Petroleum (East China), Qingdao 266580, Shandong, China

Energies, 2019, vol. 12, issue 19, 1-18

Abstract: Due to the wide application of model predictive control (MPC) in industrial processes, the assessment of MPC performance is essential to ensure product quality and improve energy efficiency. Recently, the slow feature analysis (SFA) algorithm has been successfully applied to assess the performance of MPC. However, the disadvantage of the traditional SFA-based predictable index is that it can only extract one-step predictable information in the monitored variables. In order to better mine the predictable information contained in the monitored variables with large lag, an enhanced method to assess MPC performance based on multi-step SFA (MSSFA) is proposed. Based on the relationship between the slowness of slow features (SFs) and data predictability, an MSSFA model SFA( ? ) is built through extending the temporal derivatives of the SFs from one step to multiple steps to extract multi-step predictable information in the monitored variables, which is used to construct a multi-step predictable index. Then, the predictable information in the SFs is further extracted for enhancing the multi-step predictable index to improve its sensitivity to performance changes. The effectiveness of the proposed method has been verified through two process simulation examples.

Keywords: performance assessment; model predictive controller; multi-step slow feature analysis; multi-step predictable index (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
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