Results 161 to 170 of about 263,923 (197)

An Overview of Nonlinear Model Predictive Control

2010
This chapter reviews some of the main approaches, results and open problems in Nonlinear Model Predictive Control. The style of the presentation is maintained at a high level, reducing to the minimum the mathematical details.
L. Magni, SCATTOLINI, RICCARDO
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Nonlinear Model Predictive Control: Control and Prediction Horizon

IFAC Proceedings Volumes, 2000
Abstract In this paper a new class of Model Predictive control algorithms for nonlinear systems is introduced. A prediction horizon longer than the control horizon is used. It is shown that in this way it is possible to enlarge the domain of attraction and to improve the performance without enlarging the dimension of the optimization space.
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High-speed explicit nonlinear model predictive control

2017 European Conference on Circuit Theory and Design (ECCTD), 2017
In this paper a circuit-oriented approximate solution to an explicit nonlinear Model Predictive Control (NMPC) problem is proposed, based on piecewise-affine functions defined over simplicial domain partitions. The resulting controller is suitable for circuit implementation in programmable devices, such as microcontrollers or FPGA, enabling the ...
Alberto Oliveri, Matteo Lodi, Marco Storace   +2 more
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Tube‐based robust nonlinear model predictive control

International Journal of Robust and Nonlinear Control, 2011
AbstractThis paper extends tube‐based model predictive control of linear systems to achieve robust control of nonlinear systems subject to additive disturbances. A central or reference trajectory is determined by solving a nominal optimal control problem.
Mayne, D. Q., Kerrigan, E. C., Van Wyk, E. J., Falugi, P.   +3 more
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Advanced-multi-step nonlinear model predictive control

Journal of Process Control, 2012
Abstract Nonlinear Model Predictive Control (NMPC) has gained wide attention through the application of dynamic optimization. However, this approach is susceptible to computational delay, especially if the optimization problem cannot be solved within one sampling time.
Xue Yang, Lorenz T. Biegler
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Stabilizing Predictive Control of Nonlinear ARX Models

Automatica, 1997
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
DE NICOLAO G., MAGNI L., SCATTOLINI, RICCARDO   +2 more
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Self-optimizing Robust Nonlinear Model Predictive Control

2009
This paper presents a novel method for designing robust MPC schemes that are self-optimizing in terms of disturbance attenuation. The method employs convex control Lyapunov functions and disturbance bounds to optimize robustness of the closed-loop system on-line, at each sampling instant - a unique feature in MPC.
Lazar, M., Heemels, W.P.M.H., Jokic, A.
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Adaptive control vector parameterization for nonlinear model‐predictive control

International Journal of Robust and Nonlinear Control, 2007
AbstractChemical processes are often operated in a dynamic mode. This is always true by definition for the wide class of batch processes and it also holds for the transient phases of continuous processes, caused for example by load or grade changes.
Hartwich, Arndt, Schlegel, Martin, Würth, Lynn, Marquardt, Wolfgang   +3 more
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Model Predictive Control with Internal Model for Nonlinear Systems

IFAC Proceedings Volumes, 2001
Abstract In the last years an important research effort has been made to obtain stabilizing state-feedback MPC control law with guaranteed stability for nonlinear systems, so that a relatively mature stage has just now been reached. In spite of these recent developments, for the tracking problem with guaranteed stability properties only few ...
L. Magni, SCATTOLINI, RICCARDO
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Long-prediction-horizon nonlinear model predictive control

2001
One of the advantages of long-prediction-horizon model predictive control (MPC) is its applicability to processes with nonminimum-phase behaviour. Motivated by this attractive feature of MPC, a long-prediction-horizon MPC formulation is used to derive an approximate input-output-linearising nonlinear control law for hyperbolically stable, single-input ...
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