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Hierarchical model predictive control
2007 46th IEEE Conference on Decision and Control, 2007This paper deals with the problem of designing stable hierarchical control schemes with Model Predictive Control (MPC). Specifically, at any layer of the considered structure, a robust MPC regulator is used to compute the control variables. In turn, these variables are produced by the subsystems at lower layers and must fulfill the robustness ...
COLANERI, PATRIZIO, SCATTOLINI, RICCARDO
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2021
This chapter introduces the basic concepts of Model Predictive Control (MPC) theory necessary to design the controller in later chapters. With a focus on MPC for linear systems, the design of controllers with different objective functions is covered, and some key methods such as reference tracking are presented while elaborating on implementation ...
Afonso Botelho +3 more
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This chapter introduces the basic concepts of Model Predictive Control (MPC) theory necessary to design the controller in later chapters. With a focus on MPC for linear systems, the design of controllers with different objective functions is covered, and some key methods such as reference tracking are presented while elaborating on implementation ...
Afonso Botelho +3 more
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Decentralized Model Predictive Control
2010Decentralized and distributed model predictive control (DMPC) addresses the problem of controlling a multivariable dynamical process, composed by several interacting subsystems and subject to constraints, in a computation and communication efficient way.
Bemporad, Alberto, Barcelli, Davide
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2000
Chapter 4 showed that the controllability of sheet and film processes can be quantified in terms of the accuracy of the signs of the process gains. It was also shown that constraint handling is unnecessary for many sheet and film processes, provided that the controller is designed to be robust to model uncertainties.
Andrew P. Featherstone +2 more
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Chapter 4 showed that the controllability of sheet and film processes can be quantified in terms of the accuracy of the signs of the process gains. It was also shown that constraint handling is unnecessary for many sheet and film processes, provided that the controller is designed to be robust to model uncertainties.
Andrew P. Featherstone +2 more
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Robust Model-Predictive Control
2013Model predictive control (MPC) is indisputably one of the rare modern control techniques that has significantly affected control engineering practice due to its unique ability to systematically handle constraints and optimize performance. Robust MPC (RMPC) is an improved form of the conventional MPC that is intrinsically robust in the face of ...
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2007
This chapter describes the elements that are common to all Model Predictive controllers, showing the various alternatives used in the different implementations. Some of the most popular methods will later be reviewed to demonstrate their most outstanding characteristics.
E. F. Camacho, C. Bordons
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This chapter describes the elements that are common to all Model Predictive controllers, showing the various alternatives used in the different implementations. Some of the most popular methods will later be reviewed to demonstrate their most outstanding characteristics.
E. F. Camacho, C. Bordons
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Multi-objective Model Predictive Control
2018 Joint 10th International Conference on Soft Computing and Intelligent Systems (SCIS) and 19th International Symposium on Advanced Intelligent Systems (ISIS), 2009For many real-world problems, true function form cannot be given a prior. Consequently, for example, engineering design requires experiments and/or numerical simulations to evaluate objective and constraint functions as function in terms of design variables. However, those experiments/simulations are computationally expensive.
Yeboon Yun, Hirotaka Nakayama, Min Yoon
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Cooperative distributed model predictive control
Systems & Control Letters, 2010zbMATH Open Web Interface contents unavailable due to conflicting licenses.
STEWART BT +4 more
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Well-conditioned model predictive control
ISA Transactions, 2004Model-based predictive control is an advanced control strategy that uses a move suppression factor or constrained optimization methods for achieving satisfactory closed-loop dynamic responses of complex systems. While these approaches are suitable for many processes, they are formulated on the selection of certain parameters that are ambiguous and also
Rickey, Dubay +2 more
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NEURAL MODELS IN PREDICTIVE CONTROL
IFAC Proceedings Volumes, 1995Abstract Model Based Predictive Control (MBPC) has not yet been widely used biotechnology, although in other areas successful application have been reported. This mainly because accurate nonlinear models are usually not available for biotechnological processes.
H.J.L. van Can +4 more
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