Review on model predictive control: an engineering perspective

Model-based predictive control (MPC) describes a set of advanced control methods, which make use of a process model to predict the future behavior of the controlled system. By solving a—potentially constrained—optimization problem, MPC determines the control law implicitly. This shifts the effort fo...

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Published in:	International journal of advanced manufacturing technology Vol. 117; no. 5-6; pp. 1327 - 1349
Main Authors:	Schwenzer, Max, Ay, Muzaffer, Bergs, Thomas, Abel, Dirk
Format:	Journal Article
Language:	English
Published:	London Springer London 01.11.2021 Springer Nature B.V
Subjects:	Advanced manufacturing technologies CAE) and Design Computer-Aided Engineering (CAD Control algorithms Control methods Control systems design Control theory Critical Review Design factors Engineering Feedback control Industrial and Production Engineering Mechanical Engineering Media Management Optimization Predictive control State-of-the-art reviews Model predictive control MPC Stability Computation Robustness Application Optimization
ISSN:	0268-3768, 1433-3015
Online Access:	Get full text
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Abstract	Model-based predictive control (MPC) describes a set of advanced control methods, which make use of a process model to predict the future behavior of the controlled system. By solving a—potentially constrained—optimization problem, MPC determines the control law implicitly. This shifts the effort for the design of a controller towards modeling of the to-be-controlled process. Since such models are available in many fields of engineering, the initial hurdle for applying control is deceased with MPC. Its implicit formulation maintains the physical understanding of the system parameters facilitating the tuning of the controller. Model-based predictive control (MPC) can even control systems, which cannot be controlled by conventional feedback controllers. With most of the theory laid out, it is time for a concise summary of it and an application-driven survey. This review article should serve as such. While in the beginnings of MPC, several widely noticed review paper have been published, a comprehensive overview on the latest developments, and on applications, is missing today. This article reviews the current state of the art including theory, historic evolution, and practical considerations to create intuitive understanding. We lay special attention on applications in order to demonstrate what is already possible today. Furthermore, we provide detailed discussion on implantation details in general and strategies to cope with the computational burden—still a major factor in the design of MPC. Besides key methods in the development of MPC, this review points to the future trends emphasizing why they are the next logical steps in MPC.
AbstractList	Model-based predictive control (MPC) describes a set of advanced control methods, which make use of a process model to predict the future behavior of the controlled system. By solving a—potentially constrained—optimization problem, MPC determines the control law implicitly. This shifts the effort for the design of a controller towards modeling of the to-be-controlled process. Since such models are available in many fields of engineering, the initial hurdle for applying control is deceased with MPC. Its implicit formulation maintains the physical understanding of the system parameters facilitating the tuning of the controller. Model-based predictive control (MPC) can even control systems, which cannot be controlled by conventional feedback controllers. With most of the theory laid out, it is time for a concise summary of it and an application-driven survey. This review article should serve as such. While in the beginnings of MPC, several widely noticed review paper have been published, a comprehensive overview on the latest developments, and on applications, is missing today. This article reviews the current state of the art including theory, historic evolution, and practical considerations to create intuitive understanding. We lay special attention on applications in order to demonstrate what is already possible today. Furthermore, we provide detailed discussion on implantation details in general and strategies to cope with the computational burden—still a major factor in the design of MPC. Besides key methods in the development of MPC, this review points to the future trends emphasizing why they are the next logical steps in MPC.
Author	Schwenzer, Max Ay, Muzaffer Abel, Dirk Bergs, Thomas
Author_xml	– sequence: 1 givenname: Max orcidid: 0000-0002-3422-8631 surname: Schwenzer fullname: Schwenzer, Max email: max.schwenzer@rwth-aachen.de organization: Laboratory for Machine Tools and Production Engineering (WZL), RWTH Aachen University – sequence: 2 givenname: Muzaffer surname: Ay fullname: Ay, Muzaffer organization: Institute of Automatic Control (IRT), RWTH Aachen University – sequence: 3 givenname: Thomas surname: Bergs fullname: Bergs, Thomas organization: Laboratory for Machine Tools and Production Engineering (WZL), RWTH Aachen University, Fraunhofer Institute for Production Technology IPT – sequence: 4 givenname: Dirk surname: Abel fullname: Abel, Dirk organization: Institute of Automatic Control (IRT), RWTH Aachen University
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