Results 211 to 220 of about 63,859 (262)

On optimality of nonlinear model predictive control

Systems & Control Letters, 2007
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Federico Di Palma, Lalo Magni
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Efficient nonlinear model predictive control

Proceedings of the 2000 American Control Conference. ACC (IEEE Cat. No.00CH36334), 2000
For large numbers of degrees of freedom and/or high dimensional systems, nonlinear model predictive control algorithms based on dual mode control can become intractable. This paper proposes an alternative which deploys the closed-loop paradigm that has proved effective for the case of linear time-varying or uncertain systems. The various attributes and
Mark Cannon, Basil Kouvaritakis, A. C. Brooms, Young Il Lee   +3 more
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Nonlinear model predictive control of a benchmark nonlinear boiler

2011 XXIII International Symposium on Information, Communication and Automation Technologies, 2011
This paper presents a case study of nonlinear model predictive control (NMPC) applied to a benchmark nonlinear boiler model. The motivation for NMPC is that if the controlled plant exhibits nonlinear behavior, a linear controller based on a linearized model may often be unable to achieve satisfactory performance.
Radek Horalek, Lars Imsland
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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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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 Predictive Control with Neural Models

1995
Nonlinear black-box modeling techniques are opening new horizons for modeling and control of nonlinear processes. These kind of models can be used in Model Based Predictive Control (MBPC). These techniques include Wiener Models, Fuzzy Modeling, Recurrent and Feedforward Neural networks and combinations of these.
Hubert A. B. te Braake, Henk B. Verbruggen, H. J. L. van Can   +2 more
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Nonlinear Model Predictive Control

2011
Nonlinear model predictive control (NMPC) is widely used in the process and chemical industries and increasingly for applications, such as those in the automotive industry, which use higher data sampling rates. Nonlinear Model Predictive Control is a thorough and rigorous introduction to NMPC for discrete-time and sampled-data systems.
Grüne, Lars, Pannek, Jürgen
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A Velocity Algorithm for Nonlinear Model Predictive Control

IEEE Transactions on Control Systems Technology, 2021
This brief presents a velocity-form nonlinear model predictive control (NMPC) scheme via velocity-based linearization. The main features of this approach are built-in offset-free control in the presence of disturbances, tracking of piecewise constant, possibly unreachable, reference signals, and simple implementation, as a parameterization of all ...
Pablo S. G. Cisneros, Herbert Werner
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Stable model predictive control for a nonlinear system

Journal of the Franklin Institute, 2011
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Akbar Rahideh, M. Hasan Shaheed
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Nonlinear Model Predictive Control

2007
In general, industrial processes are nonlinear, but, as has been shown in this book, most MPC applications are based on the use of linear models. There are two main reasons for this: on one hand, the identification of a linear model based on process data is relatively easy and, on the other hand, linear models provide good results when the plant is ...
E. F. Camacho, C. Bordons
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