{"title":"Economic Performance of Model Predictive Control at Back-off Operating Point","authors":"Nabil Magbool Jan , Sridharakumar Narasimhan","doi":"10.1016/j.jprocont.2024.103231","DOIUrl":null,"url":null,"abstract":"<div><p>In this paper, we address the economic performance of Model Predictive Control (MPC) while operating at a backed-off operating point. Operating the plant at a constrained optimal point will often cause constraint violations due to uncertainties such as disturbances and measurement errors, etc. To ensure dynamic feasibility, the concept of economic back-off is used. In this work, we select the set point as the economic back-off point such that the dynamic operating region should have the least variability in the active constrained variables while ensuring the feasibility of other variables. In other words, the dynamic operating region is oriented by the proper design of a controller such that variability in active constrained variables is as low as possible. This controller design can be transformed into equivalent objective function weights of the MPC controller. In this study, we demonstrate that the determined back-off point is optimal for both linear controller and MPC controller when there are no unconstrained degrees of freedom. For the case with unconstrained degrees of freedom, the back-off point determined using the presented approach is optimal only for a linear controller but suboptimal for an MPC controller. Demonstrative case studies are presented to illustrate the economic performance of the MPC controller at the determined economic back-off point.</p></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":null,"pages":null},"PeriodicalIF":3.3000,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Process Control","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0959152424000714","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
In this paper, we address the economic performance of Model Predictive Control (MPC) while operating at a backed-off operating point. Operating the plant at a constrained optimal point will often cause constraint violations due to uncertainties such as disturbances and measurement errors, etc. To ensure dynamic feasibility, the concept of economic back-off is used. In this work, we select the set point as the economic back-off point such that the dynamic operating region should have the least variability in the active constrained variables while ensuring the feasibility of other variables. In other words, the dynamic operating region is oriented by the proper design of a controller such that variability in active constrained variables is as low as possible. This controller design can be transformed into equivalent objective function weights of the MPC controller. In this study, we demonstrate that the determined back-off point is optimal for both linear controller and MPC controller when there are no unconstrained degrees of freedom. For the case with unconstrained degrees of freedom, the back-off point determined using the presented approach is optimal only for a linear controller but suboptimal for an MPC controller. Demonstrative case studies are presented to illustrate the economic performance of the MPC controller at the determined economic back-off point.
期刊介绍:
This international journal covers the application of control theory, operations research, computer science and engineering principles to the solution of process control problems. In addition to the traditional chemical processing and manufacturing applications, the scope of process control problems involves a wide range of applications that includes energy processes, nano-technology, systems biology, bio-medical engineering, pharmaceutical processing technology, energy storage and conversion, smart grid, and data analytics among others.
Papers on the theory in these areas will also be accepted provided the theoretical contribution is aimed at the application and the development of process control techniques.
Topics covered include:
• Control applications• Process monitoring• Plant-wide control• Process control systems• Control techniques and algorithms• Process modelling and simulation• Design methods
Advanced design methods exclude well established and widely studied traditional design techniques such as PID tuning and its many variants. Applications in fields such as control of automotive engines, machinery and robotics are not deemed suitable unless a clear motivation for the relevance to process control is provided.