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Books on the topic 'Nonlinear predictive control'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

Allgöwer, Frank. Nonlinear Model Predictive Control. Birkhäuser Basel, 2000.

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2

Allgöwer, Frank, and Alex Zheng, eds. Nonlinear Model Predictive Control. Birkhäuser Basel, 2000. http://dx.doi.org/10.1007/978-3-0348-8407-5.

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3

Grüne, Lars, and Jürgen Pannek. Nonlinear Model Predictive Control. Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-501-9.

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4

Magni, Lalo, Davide Martino Raimondo, and Frank Allgöwer, eds. Nonlinear Model Predictive Control. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01094-1.

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5

Grüne, Lars, and Jürgen Pannek. Nonlinear Model Predictive Control. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-46024-6.

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6

Grancharova, Alexandra, and Tor Arne Johansen. Explicit Nonlinear Model Predictive Control. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28780-0.

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7

Ławryńczuk, Maciej. Nonlinear Predictive Control Using Wiener Models. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-83815-7.

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8

Basil, Kouvaritakis, Cannon Mark 1971-, and Institution of Electrical Engineers, eds. Nonlinear predictive control: Theory and practice. Institution of Electrical Engineers, 2001.

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9

Albin Rajasingham, Thivaharan. Nonlinear Model Predictive Control of Combustion Engines. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68010-7.

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10

Jürgen, Pannek, ed. Nonlinear model predictive control: Theory and algorithms. Springer, 2011.

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11

service), SpringerLink (Online, ed. Nonlinear model predictive control: Towards new challenging applications. Springer, 2009.

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12

Yu, Qiongxia, Ting Lei, Fengchen Tian, Zhongsheng Hou, and Xuhui Bu. Predictive Learning Control for Unknown Nonaffine Nonlinear Systems. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-8857-8.

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13

Liu, G. P. Neural network based predictive control for nonlinear systems. University of Sheffield, Dept. of Automatic Control and Systems Engineering, 1996.

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14

Grancharova, Alexandra. Explicit Nonlinear Model Predictive Control: Theory and Applications. Springer Berlin Heidelberg, 2012.

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15

Germany), NMPC'05 (2005 Freudenstadt. Assessment and future directions of nonlinear model predictive control. Springer, 2007.

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16

Findeisen, Rolf, Frank Allgöwer, and Lorenz T. Biegler, eds. Assessment and Future Directions of Nonlinear Model Predictive Control. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-72699-9.

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17

Kirches, Christian. Fast Numerical Methods for Mixed-Integer Nonlinear Model-Predictive Control. Vieweg+Teubner Verlag, 2011. http://dx.doi.org/10.1007/978-3-8348-8202-8.

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18

Christofides, Panagiotis D. Networked and Distributed Predictive Control: Methods and Nonlinear Process Network Applications. Springer-Verlag London Limited, 2011.

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19

Allgower, Frank. Nonlinear Model Predictive Control. Springer, 2012.

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20

Nonlinear Model Predictive Control. Springer, 2011.

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21

Lalo Magni,Davide Martino Raimondo,Frank Allg Wer. Nonlinear Model Predictive Control. Springer, 2009.

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22

Faulwasser, Timm, Matthias A. Müller, and Lars Grüne. Economic Nonlinear Model Predictive Control. Now Publishers, 2018.

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23

Levine, William S., and Saša V. Raković. Handbook of Model Predictive Control. Birkhäuser, 2018.

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24

Grüne, Lars, and Jürgen Pannek. Nonlinear Model Predictive Control: Theory and Algorithms. Springer, 2018.

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25

Grüne, Lars, and Jürgen Pannek. Nonlinear Model Predictive Control: Theory and Algorithms. Springer, 2016.

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26

Oliveira, Simone Loureiro De. Model Predictive Control for Constrained Nonlinear Systems. vdf Hochschulverlag ETH Zurich, 1996.

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27

Nonlinear Model Predictive Control: Theory and Algorithms. Springer, 2017.

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28

Cannon, Mark, and Basil Kouvaritakis. Model Predictive Control: Classical, Robust and Stochastic. Springer International Publishing AG, 2015.

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29

Cannon, Mark, and Basil Kouvaritakis. Model Predictive Control: Classical, Robust and Stochastic. Springer, 2015.

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30

Cannon, Mark, and Basil Kouvaritakis. Model Predictive Control: Classical, Robust and Stochastic. Springer, 2019.

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31

(Editor), Frank Allgöwer, and Alex Zheng (Editor), eds. Nonlinear Model Predictive Control (Progress in Systems and Control Theory). Birkhäuser Basel, 2000.

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32

Explicit Nonlinear Model Predictive Control Theory And Applications. Springer, 2012.

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33

(Editor), Basil Kouvaritakis, and Mark Cannon (Editor), eds. Non-Linear Predictive Control: Theory & Practice (Iee Control Series, 61). Institution Electrical Engineers, 2001.

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34

Cannon, Mark, and Basil Kouvaritakis. Non-Linear Predictive Control: Theory and Practice. Institution of Engineering & Technology, 2011.

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35

Adetola, Veronica, Darryl DeHaan, and Martin Guay. Robust and Adaptive Model Predictive Control of Nonlinear Systems. Institution of Engineering & Technology, 2016.

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36

Robust And Adaptive Model Predictive Control Of Nonlinear Systems. Institution of Engineering & Technology (IET), 2012.

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37

(Editor), Frank Allgower, and Alex Zheng (Editor), eds. Nonlinear Model Predictive Control (Progress in Systems and Control Theory, V. 26). Birkhauser, 2000.

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38

Constrained Model Predictive Control of a Nonlinear Aerospace System. Storming Media, 1997.

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39

Allgöwer, Frank, Rolf Findeisen, and Lorenz Biegler. Assessment and Future Directions of Nonlinear Model Predictive Control. Springer London, Limited, 2007.

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40

Christofides, Panagiotis D., Jinfeng Liu, and David Muñoz de la Peña. Networked and Distributed Predictive Control. Springer, 2011.

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41

Kirches, Christian. Fast Numerical Methods for Mixed-Integer Nonlinear Model-Predictive Control. Vieweg Verlag, Friedr, & Sohn Verlagsgesellschaft mbH, 2011.

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42

Christofides, Panagiotis D., Jinfeng Liu, and David Muñoz de la Peña. Networked and Distributed Predictive Control: Methods and Nonlinear Process Network Applications. Springer London, Limited, 2013.

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43

Yu, Qiongxia, Fengchen Tian, and Xuhui Bu. Predictive Learning Control for Unknown Nonaffine Nonlinear Systems: Theory and Applications. Springer, 2023.

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44

Rajasingham, Thivaharan Albin. Nonlinear Model Predictive Control of Combustion Engines: From Fundamentals to Applications. Springer International Publishing AG, 2021.

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45

Rajasingham, Thivaharan Albin. Nonlinear Model Predictive Control of Combustion Engines: From Fundamentals to Applications. Springer International Publishing AG, 2022.

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46

Yu, Qiongxia, Fengchen Tian, and Xuhui Bu. Predictive Learning Control for Unknown Nonaffine Nonlinear Systems: Theory and Applications. Springer, 2024.

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47

Maciej Ławryńczuk. Nonlinear Predictive Control Using Wiener Models: Computationally Efficient Approaches for Polynomial and Neural Structures. Springer International Publishing AG, 2022.

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48

Ławryńczuk, Maciej. Nonlinear Predictive Control Using Wiener Models: Computationally Efficient Approaches for Polynomial and Neural Structures. Springer International Publishing AG, 2021.

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49

(Editor), Rolf Findeisen, Frank Allgöwer (Editor), and Lorenz Biegler (Editor), eds. Assessment and Future Directions of Nonlinear Model Predictive Control (Lecture Notes in Control and Information Sciences) (Lecture Notes in Control and Information Sciences). Springer, 2007.

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50

Ferrari, Matthew. Using disease dynamics and modeling to inform control strategies in low-income countries. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198789833.003.0008.

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Abstract:

The incidence infectious disease is inherently dynamic in time and space. Mathematical models that account for the dynamic processes that give rise to fluctuations in disease incidence are powerful tools in disease management and control. We describe the use of dynamic models for surveillance, evaluation and prediction of disease control efforts in low-income countries. Dynamic models can help to anticipate trends owing to intrinsic (e.g., herd immunity) or extrinsic (e.g., seasonality) forces that may confound efforts to isolate the impact of specific interventions. Infectious disease dynamic

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