干旱区生态环境知识资源中心(Arid): A data-driven framework integrating Lyapunov-based MPC and OASIS-based observer for control beyond training domains

Arid

DOI	10.1016/j.jprocont.2024.103224
	A data-driven framework integrating Lyapunov-based MPC and OASIS-based observer for control beyond training domains
	Bhadriraju, Bhavana; Kwon, Joseph Sang-Il; Khan, Faisal
通讯作者	Kwon, JSI
来源期刊	JOURNAL OF PROCESS CONTROL
ISSN	0959-1524
EISSN	1873-2771
出版年	2024
卷号	138
英文摘要	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 datadriven 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 machinebased 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.
英文关键词	Adaptive data-driven model Domain of applicability Input-to-state stability Lyapunov function Model predictive control Observer
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:001240120700001
WOS关键词	SPARSE IDENTIFICATION ; NONLINEAR-SYSTEMS ; PREDICTIVE CONTROL ; STABILIZATION ; REGRESSION ; DYNAMICS ; STATE
WOS类目	Automation & Control Systems ; Engineering, Chemical
WOS研究方向	Automation & Control Systems ; Engineering
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/404676
推荐引用方式 GB/T 7714	Bhadriraju, Bhavana,Kwon, Joseph Sang-Il,Khan, Faisal. A data-driven framework integrating Lyapunov-based MPC and OASIS-based observer for control beyond training domains[J],2024,138.
APA	Bhadriraju, Bhavana,Kwon, Joseph Sang-Il,&Khan, Faisal.(2024).A data-driven framework integrating Lyapunov-based MPC and OASIS-based observer for control beyond training domains.JOURNAL OF PROCESS CONTROL,138.
MLA	Bhadriraju, Bhavana,et al."A data-driven framework integrating Lyapunov-based MPC and OASIS-based observer for control beyond training domains".JOURNAL OF PROCESS CONTROL 138(2024).