Academic literature on the topic 'Nonlinear predictive control'

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Journal articles on the topic "Nonlinear predictive control"

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Magni, L. "Nonlinear Model Predictive Control: Control and Prediction Horizon." IFAC Proceedings Volumes 33, no. 13 (2000): 213–18. http://dx.doi.org/10.1016/s1474-6670(17)37192-6.

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PATWARDHAN, ASHUTOSH A., JAMES B. RAWLINGS, and THOMAS F. EDGAR. "NONLINEAR MODEL PREDICTIVE CONTROL." Chemical Engineering Communications 87, no. 1 (1990): 123–41. http://dx.doi.org/10.1080/00986449008940687.

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Katende, Edward, Arthur Jutan, and Rob Corless. "Quadratic Nonlinear Predictive Control." Industrial & Engineering Chemistry Research 37, no. 7 (1998): 2721–28. http://dx.doi.org/10.1021/ie970754v.

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Zhang, Mengwei, Zhixiang Lin, Haiyang Huang, and Tianhong Zhang. "Design and verification of model predictive control for micro-turboshaft engine." Advances in Mechanical Engineering 11, no. 12 (2019): 168781401989019. http://dx.doi.org/10.1177/1687814019890198.

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In this article, a nonlinear model predictive control algorithm for a micro-turboshaft engine is designed. The control effect is verified by a bench test. First, a micro-turboshaft engine test bench is built, and the open-loop control experiment was carried out on it. Based on experiment data, a linear parameter varying prediction model is established. Then, by online rolling optimization based on multistep output prediction, together with feedback correction, a nonlinear model predictive control algorithm is obtained. The influence of algorithm parameters on the control effect is studied, and
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Soroush, Masoud, and Masoud Nikravesh. "Shortest-Prediction Horizon Nonlinear Model Predictive Control 1." IFAC Proceedings Volumes 29, no. 1 (1996): 5817–22. http://dx.doi.org/10.1016/s1474-6670(17)58611-5.

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Xu, Zhi Cheng, Bin Zhu, and Qing Bin Jiang. "Application of Neural Network for Nonlinear Predictive Control." Advanced Materials Research 562-564 (August 2012): 1964–67. http://dx.doi.org/10.4028/www.scientific.net/amr.562-564.1964.

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A novel model predictive control method was proposed for a class of dynamic processes with modest nonlinearities in this paper. In this method, a diagonal recurrent neural network (DRNN) is used to compensate nonlinear modeling error that is caused because linear model is regarded as prediction model of nonlinear process. It is aimed at offsetting the effect of model mismatch on the control performance, strengthening the robustness of predictive control and the stability of control system. Under a certain assumption condition, linear model predictive control method is extended to nonlinear pro
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Faulwasser, Timm, Lars Grüne, and Matthias A. Müller. "Economic Nonlinear Model Predictive Control." Foundations and Trends® in Systems and Control 5, no. 1 (2018): 224–409. http://dx.doi.org/10.1561/2600000014.

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Crassidis, John L., F. Landis Markley, Tobin C. Anthony, and Stephen F. Andrews. "Nonlinear Predictive Control of Spacecraft." Journal of Guidance, Control, and Dynamics 20, no. 6 (1997): 1096–103. http://dx.doi.org/10.2514/2.4191.

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Xi, Yu-Geng, Fan Wang, and Guo-Hua Wu. "Nonlinear Multi-Model Predictive Control." IFAC Proceedings Volumes 29, no. 1 (1996): 2359–64. http://dx.doi.org/10.1016/s1474-6670(17)58026-x.

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Paulusová, Jana, Štefan Kozák, and Jakub Grošek. "Nonlinear Model-Based Predictive Control." IFAC Proceedings Volumes 36, no. 18 (2003): 171–75. http://dx.doi.org/10.1016/s1474-6670(17)34664-5.

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Dissertations / Theses on the topic "Nonlinear predictive control"

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Sriniwas, Ganti Ravi. "Nonlinear model predictive control." Diss., Georgia Institute of Technology, 1995. http://hdl.handle.net/1853/10267.

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Youssef, Ahmed Medhat Mohamed. "Nonlinear predictive flight control system design." Thesis, University of Strathclyde, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.401502.

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Siller-Alcalá, Irma Irasema. "Nonlinear continuous-time generalised predictive control." Thesis, University of Glasgow, 1998. http://theses.gla.ac.uk/2090/.

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The development of the nonlinear version of the Continuous-time Generalised Predictive Control (NCGPC) is presented. Unlike the linear version, the nonlinear version is developed in state-space form and shown to include Nonlinear Generalised Minimum Variance (NGMV), and a new algorithm, Nonlinear Predictive Generalised Minimum Variance (NPGMV), as special cases. Through simulations, it is demonstrated that NCGPC can deal with nonlinear systems whose relative degree is not well defined and nonlinear systems with unstable zero dynamics. Geometric approaches, such as exact linearisation, are show
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Simminger, Jerome C. "A constrained multivariable nonlinear predictive controller." Thesis, Georgia Institute of Technology, 1991. http://hdl.handle.net/1853/10152.

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Savvidis, Petros. "Nonlinear control : an LPV nonlinear predictive generalised minimum variance perspective." Thesis, University of Strathclyde, 2017. http://digitool.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=27947.

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This thesis describes new developments in nonlinear controllers for industrial applications. It first introduces the Nonlinear Generalised Minimum Variance (NGMV) control algorithm, for Linear Parameter Varying systems (LPV). This combines the benefits of the basic NGMV algorithm in dealing with nonlinearities, where a black box input model can be used, and adds an option to also approximate a nonlinear system with an LPV output subsystem. The models can therefore represent LPV systems and characteristics including saturation, discontinuities and time-varying dynamics. The next major contribut
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Fannemel, Åsmund Våge. "Dynamic Positioning by Nonlinear Model Predictive Control." Thesis, Norwegian University of Science and Technology, Department of Engineering Cybernetics, 2008. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-8921.

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<p>This thesis discusses the theoretical aspects of the unscented Kalman filter (UKF) and nonlinear model predictive control (NMPC) and try to evaluate their practical value in a dynamic positioning (DP) system. A nonlinear horizontal vessel model is used as the basis for performing state, disturbance, and parameter estimation, and attempts at controling the vessel using NMPC are made. It is shown that the extended Kalman filter (EKF), which is much used in various navigation applications including DP, is outperformed both theoretically and practically in simulations by the UKF. Much of which
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Al, Seyab Rihab Khalid Shakir. "Nonlinear model predictive control using automatic differentiation." Thesis, Cranfield University, 2006. http://hdl.handle.net/1826/1491.

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Although nonlinear model predictive control (NMPC) might be the best choice for a nonlinear plant, it is still not widely used. This is mainly due to the computational burden associated with solving online a set of nonlinear differential equations and a nonlinear dynamic optimization problem in real time. This thesis is concerned with strategies aimed at reducing the computational burden involved in different stages of the NMPC such as optimization problem, state estimation, and nonlinear model identification. A major part of the computational burden comes from function and derivative evaluati
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Bal, Llanjun. "Nonlinear Predictive Control Based On NARMAX Models." Thesis, University of Sheffield, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.522535.

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Balbis, Luisella. "Nonlinear model predictive control for industrial applications." Thesis, University of Strathclyde, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.501892.

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Breyholtz, Øyvind. "Nonlinear Model Predictive Pressure Control during Drilling Operations." Thesis, Norwegian University of Science and Technology, Department of Engineering Cybernetics, 2008. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-9697.

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<p>Drilling into mature, depleted fields is often difficult because of tight pressure margins. Increasing the pressure control will enable wells that previously were considered undrillable, to be drilled. Enabling drilling and increased oil recovery from depleted fields would most likely lead to a substantial increase in profit margains. A better pressure control will also increase the safety of the drilling crew, because the risk of unwanted situations such as a kick or a blow-out is decreased, also reducing the risk of unwanted environmental influence, e.g. oil spill. To compensate for th
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Books on the topic "Nonlinear predictive control"

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

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

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Book chapters on the topic "Nonlinear predictive control"

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Tatjewski, Piotr, and Maciej Ławryńczuk. "Nonlinear Predictive Control." In Automatic Control, Robotics, and Information Processing. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-48587-0_7.

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Camacho, E. F., and C. Bordons. "Nonlinear Model Predictive Control." In Model Predictive control. Springer London, 2007. http://dx.doi.org/10.1007/978-0-85729-398-5_9.

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Grüne, Lars, and Jürgen Pannek. "Nonlinear Model Predictive Control." In Nonlinear Model Predictive Control. Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-501-9_3.

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Grüne, Lars, and Jürgen Pannek. "Nonlinear Model Predictive Control." In Nonlinear Model Predictive Control. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46024-6_3.

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Kang, Keunmo, and Robert R. Bitmead. "Model Predictive Control with Control Lyapunov Function Support." In Nonlinear Model Predictive Control. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01094-1_6.

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Grancharova, Alexandra, and Tor Arne Johansen. "Nonlinear Model Predictive Control." In Explicit Nonlinear Model Predictive Control. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28780-0_2.

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Soroush, Masoud, and Kenneth R. Muske. "Analytical Model Predictive Control." In Nonlinear Model Predictive Control. Birkhäuser Basel, 2000. http://dx.doi.org/10.1007/978-3-0348-8407-5_9.

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Grüne, Lars, and Jürgen Pannek. "Infinite Horizon Optimal Control." In Nonlinear Model Predictive Control. Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-501-9_4.

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Grüne, Lars, and Jürgen Pannek. "Infinite Horizon Optimal Control." In Nonlinear Model Predictive Control. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46024-6_4.

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Quevedo, Daniel E., Anders Ahlén, and Graham C. Goodwin. "Predictive Power Control of Wireless Sensor Networks for Closed Loop Control." In Nonlinear Model Predictive Control. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01094-1_17.

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Conference papers on the topic "Nonlinear predictive control"

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Chanfreut, Paula, José M. Maestre, Quanyan Zhu, and W. P. M. H. Maurice Heemels. "Cooperative nonlinear distributed model predictive control with dissimilar control horizons." In 2024 IEEE 63rd Conference on Decision and Control (CDC). IEEE, 2024. https://doi.org/10.1109/cdc56724.2024.10886159.

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Ebrahim, A. Al-Gallaf. "Intelligent nonlinear predictive control." In 2006 IEEE GCC Conference. IEEE, 2006. http://dx.doi.org/10.1109/ieeegcc.2006.5686185.

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"Nonlinear model predictive control." In Proceedings of the 1999 American Control Conference. IEEE, 1999. http://dx.doi.org/10.1109/acc.1999.782912.

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Kelman, Anthony, and Francesco Borrelli. "Parallel nonlinear predictive control." In 2012 50th Annual Allerton Conference on Communication, Control, and Computing (Allerton). IEEE, 2012. http://dx.doi.org/10.1109/allerton.2012.6483201.

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Halldorsson, U., M. Fikar, and H. Unbehauen. "Multirate nonlinear predictive control." In Proceedings of 2002 American Control Conference. IEEE, 2002. http://dx.doi.org/10.1109/acc.2002.1024589.

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Grimble, Michael J., and Pawel Majecki. "Nonlinear Predictive GMV control." In 2008 American Control Conference (ACC '08). IEEE, 2008. http://dx.doi.org/10.1109/acc.2008.4586654.

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Kapernick, Bartosz, Sebastian Suss, Endric Schubert, and Knut Graichen. "A synthesis strategy for nonlinear model predictive controller on FPGA." In 2014 UKACC 10th International Conference on Control (CONTROL). IEEE, 2014. http://dx.doi.org/10.1109/control.2014.6915218.

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Ceassidis, John, F. Markley, Tobin Anthony, et al. "Nonlinear predictive control of spacecraft." In 35th Aerospace Sciences Meeting and Exhibit. American Institute of Aeronautics and Astronautics, 1997. http://dx.doi.org/10.2514/6.1997-114.

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Cannon, M., B. Kouvaritakis, A. C. Brooms, and Y. I. Lee. "Efficient nonlinear model predictive control." In Proceedings of 2000 American Control Conference (ACC 2000). IEEE, 2000. http://dx.doi.org/10.1109/acc.2000.878857.

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Liu, X. J., L. X. Niu, and J. Z. Liu. "Nonlinear multivariable supervisory predictive control." In 2009 American Control Conference. IEEE, 2009. http://dx.doi.org/10.1109/acc.2009.5160006.

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Reports on the topic "Nonlinear predictive control"

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Nishimura, Masatsugu, Yoshitaka Tezuka, Enrico Picotti, Mattia Bruschetta, Francesco Ambrogi, and Toru Yoshii. Study of Rider Model for Motorcycle Racing Simulation. SAE International, 2020. http://dx.doi.org/10.4271/2019-32-0572.

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Various rider models have been proposed that provide control inputs for the simulation of motorcycle dynamics. However, those models are mostly used to simulate production motorcycles, so they assume that all motions are in the linear region such as those in a constant radius turn. As such, their performance is insufficient for simulating racing motorcycles that experience quick acceleration and braking. Therefore, this study proposes a new rider model for racing simulation that incorporates Nonlinear Model Predictive Control. In developing this model, it was built on the premise that it can c
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NUMERICAL SIMULATION ANALYSIS OF TEMPERATURE FIELD OF BOX-TYPE COMPOSITE WALL. The Hong Kong Institute of Steel Construction, 2022. http://dx.doi.org/10.18057/icass2020.p.321.

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The spatial and temporal characteristics of the internal temperature field of the box-type composite wall have great significance to high temperature mechanical response and fire resistance performance of composite wall subjected to fire. A series of assumptions for the simplified model were made after determining the influence of various thermal parameters, the boundary conditions of surface and the contact conditions. And the finite element software(ABAQUS) was used to establish the temperature field analysis model to conduct nonlinear transient temperature response analysis, then the validi
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