Sparse Identification and Nonlinear Model Predictive Control for Diesel Engine Air Path System

Yahagi, Shuichi; Seto, Hiroki; Yonezawa, Ansei; Kajiwara, Itsuro

Sparse Identification and Nonlinear Model Predictive Control for Diesel Engine Air Path System

Yahagi, Shuichi; Seto, Hiroki; Yonezawa, Ansei et al.

International journal of control, automation, and systems, 2025, 23(2), 620-629

Permalink : https://hdl.handle.net/2115/97453

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論文情報

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アクセス権	open access
URI	https://hdl.handle.net/2115/97453
タイトル	Sparse Identification and Nonlinear Model Predictive Control for Diesel Engine Air Path System
著者	Yahagi, Shuichi ; Seto, Hiroki ; Yonezawa, Ansei ; Kajiwara, Itsuro
著者名 Yahagi, Shuichi
著者名 Seto, Hiroki
e-Rad_Researcher 30979737 著者名 Yonezawa, Ansei
e-Rad_Researcher 60224416 著者名 Kajiwara, Itsuro
言語	英語
キーワード	Air path system
	diesel engine
	model predictive control
	sparse identification
発行日	2025-02-05
出版者	Institute of Control, Robotics and Systems (ICROS) , The Korean Institute of Electrical Engineers (KIEE)
収録物名	International journal of control, automation, and systems
巻(号)	23(2)
ページ	620-629
抄録	This paper presents a sparse identification of nonlinear dynamic systems (SINDy) for a diesel engine air path system and nonlinear model predictive control (NMPC) with the SINDy model to attain good control performance. The air path system control is well known as a challenging problem, and many studies have been presented such as traditional model-based control design and machine learning. However, these conventional approaches still have some difficulties including the control performance and design costs. In this paper, we obtain the model of the air path system in a data-driven manner using the SINDy algorithm and construct the offset-free NMPC with the SINDy model. SINDy is a suitable modeling method for controlling a complicated air path system, owing to its characteristics of high computational efficiency, high learning efficiency, high modeling accuracy, and applicability to complex systems. Additionally, NMPC provides high control performance under constraints. The proposed offset-free NMPC with the SINDy model is verified through the simulations. The results show that the coefficient of determination of the SINDy model provided over 90%, and the controller performance of the NMPC was better than that of the traditional robust controller and satisfied the constraints.
資源タイプ	学術雑誌論文
出版タイプ	Accepted Manuscript
権利情報	This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/s12555-024-0452-9
PISSN	1598-6446
関連情報 (isVersionOf)	出版社版DOI https://doi.org/10.1007/s12555-024-0452-9