Bhavana Bhadriraju , Joseph Sang-Il Kwon , Faisal Khan
{"title":"A data-driven framework integrating Lyapunov-based MPC and OASIS-based observer for control beyond training domains","authors":"Bhavana Bhadriraju , Joseph Sang-Il Kwon , Faisal Khan","doi":"10.1016/j.jprocont.2024.103224","DOIUrl":null,"url":null,"abstract":"<div><p>Due to their predictive capabilities and computational efficiency, data-driven models are often employed in model predictive controller (MPC) design. These models offer precise predictions within their training domains, which aids in effective process control. However, real-world processes frequently experience operational changes, requiring control under new conditions that can lie beyond the training domains of existing data-driven models. Developing new models for these scenarios is challenging due to limited historical data. To address this limitation, we develop a novel data-driven control framework integrating an adaptive modeling approach called operable adaptive sparse identification of systems (OASIS) with the Luenberger observer. Firstly, we train the OASIS model and identify its domain of applicability (DA) using a support vector machine-based classifier. Subsequently, we formulate a Lyapunov-based MPC that relies on the OASIS model within the DA and the OASIS-based observer model beyond the DA. Additionally, we establish theoretical guarantees on the input-to-state stability of the observer, along with analyzing the stabilizability and recursive feasibility of the designed LMPC. The developed framework enhances the applicability of data-driven process control in diverse operating conditions. We highlighted its effectiveness using a chemical reactor example.</p></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":null,"pages":null},"PeriodicalIF":3.3000,"publicationDate":"2024-05-08","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/S0959152424000647","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
Due to their predictive capabilities and computational efficiency, data-driven models are often employed in model predictive controller (MPC) design. These models offer precise predictions within their training domains, which aids in effective process control. However, real-world processes frequently experience operational changes, requiring control under new conditions that can lie beyond the training domains of existing data-driven models. Developing new models for these scenarios is challenging due to limited historical data. To address this limitation, we develop a novel data-driven control framework integrating an adaptive modeling approach called operable adaptive sparse identification of systems (OASIS) with the Luenberger observer. Firstly, we train the OASIS model and identify its domain of applicability (DA) using a support vector machine-based classifier. Subsequently, we formulate a Lyapunov-based MPC that relies on the OASIS model within the DA and the OASIS-based observer model beyond the DA. Additionally, we establish theoretical guarantees on the input-to-state stability of the observer, along with analyzing the stabilizability and recursive feasibility of the designed LMPC. The developed framework enhances the applicability of data-driven process control in diverse operating conditions. We highlighted its effectiveness using a chemical reactor example.
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.