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

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(τ) 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.

Performance Assessment of Predictive Control—A Survey

“…An interesting approach using novel, multivariate statistical technology called Slow Feature Analysis (SFA) has been proposed to separate temporal slow features from process variables. It was firstly used for diagnostics, and then extended to the MPC assessment task [154], lately followed by further modifications [155,156]. The approach enables monitoring both steady-state and dynamic responses.…”