Interactive visualization for diagnosis of industrial Model Predictive Controllers with steady-state optimizers. (April 2022)

• Record Type: Journal Article
• Authors: Elnawawi, Shams; Siang, Lim C.; O'Connor, Daniel L.; Gopaluni, R. Bhushan
• Is Part Of: Control engineering practice. Volume 121(2022)
• Publication Date: 2022-04
• Subjects: Data visualization -- Model predictive control -- Performance monitoring -- Linear Programming; Automatic control -- Periodicals; 629.89
• DOI: 10.1016/j.conengprac.2021.105056
• Languages: English
• ISSNs: 0967-0661

Abstract

Abstract: Model Predictive Controllers (MPCs) are widely used in the process industries and are typically implemented with an integrated Linear Program (LP) optimizer in the form of two-stage LP-MPC systems. Despite significant control-theoretic advances in MPC design and performance evaluation in academia, there is still a gap in addressing operational issues in real-world MPC controllers. In particular, engineers and operators responsible for sustaining MPCs often need to interpret the LP solution to understand the controller's actions. Without easy interpretability, it is difficult to troubleshoot MPCs especially for large-dimensional controllers. To alleviate this difficulty, a systematic approach that facilitates LP solution diagnostics using tools from data visualization and process control is developed. The 'partial pivoting' operation – an industrial practice that has seen limited exposure in academic literature – is discussed in detail with regards to its role in LP solution diagnosis. Typical workflows for diagnosing problematic controllers are used in conjunction with data visualization principles to guide the design of new tools focused on visualizing variable constraint data that facilitate the diagnosis process. These proposed tools are designed using Munzner's "Nested Model" as a guiding framework for visualization design and evaluation. The use of these tools is demonstrated in multiple industrial examples, with comparison to current industrial methodologies. Graphical abstract: Highlights: Operational issues of industrial MPCs have not been an academic focus. Conventional, static views of large volumes of MPC data are not user-friendly. The LP solution can help streamline MPC diagnosis by removing irrelevant variables. An interactive heatmap is developed to visualize time-varying variable constraints. 2 industrial case studies are used to demonstrate the benefits of these tools.