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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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Learning-Based Model Predictive Control for Autonomous Racing
IEEE Robotics and Automation Letters, 2019In this letter, we present a learning-based control approach for autonomous racing with an application to the AMZ Driverless race car gotthard. One major issue in autonomous racing is that accurate vehicle models that cover the entire performance ...
Juraj Kabzan +3 more
semanticscholar +1 more source
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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Model Controlled Prediction: A Reciprocal Alternative of Model Predictive Control
IEEE/CAA Journal of Automatica Sinica, 2022Li, Shen, Liu, Yang, Qu, Xiaobo
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2008 Chinese Control and Decision Conference, 2008
It is shown that the existing methods of designing stable predictive controller fail to stabilize the closed loop system for processes with repetitive unstable poles (minimum or non-minimum phase) even though the required conditions are satisfied.
Mahdi Yaghoobi, Mohammad Haeri
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It is shown that the existing methods of designing stable predictive controller fail to stabilize the closed loop system for processes with repetitive unstable poles (minimum or non-minimum phase) even though the required conditions are satisfied.
Mahdi Yaghoobi, Mohammad Haeri
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A multirate model-based predictive controller
IEEE Transactions on Automatic Control, 1995Model based predictive control (MBPC) is an emerging design tool in process control, thanks to its capability to incorporate several attributes fundamental in practical applications. On the other hand, multirate control systems are able to describe various practical situations, where technological constraints require that sensor measurements and ...
SCATTOLINI, RICCARDO +1 more
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Model Predictive Control Tuning by Controller Matching
IEEE Transactions on Automatic Control, 2010The effectiveness of model predictive control (MPC) in dealing with input and state constraints during transient operations is well known. However, in contrast with several linear control techniques, closed-loop frequency-domain properties such as sensitivities and robustness to small perturbations are usually not taken into account in the MPC design ...
Di Cairano, Stefano, Bemporad, Alberto
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2004
Model predictive control is the most important control technique used in industry for multivariable systems. It allows to take into account constraints on the manipulated inputs, their moves and even the controlled outputs. It is described under different forms, as dynamic matrix control and quadratic dynamic matrix control.
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Model predictive control is the most important control technique used in industry for multivariable systems. It allows to take into account constraints on the manipulated inputs, their moves and even the controlled outputs. It is described under different forms, as dynamic matrix control and quadratic dynamic matrix control.
openaire +1 more source
Constrained model predictive control: Stability and optimality
at - Automatisierungstechnik, 2000David Q. Mayne +3 more
semanticscholar +1 more source
2012
This chapter analyses the behaviour of the plant under generalised predictive control where the power plant is modelled as a MIMO linear and nonlinear system. The nonlinear model includes elastic water column effects. The response of the system with these controllers is compared with classical controllers.
German Ardul Munoz-Hernandez +2 more
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This chapter analyses the behaviour of the plant under generalised predictive control where the power plant is modelled as a MIMO linear and nonlinear system. The nonlinear model includes elastic water column effects. The response of the system with these controllers is compared with classical controllers.
German Ardul Munoz-Hernandez +2 more
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