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Relevant bibliographies by topics / Non-linear predictive control

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Academic literature on the topic 'Non-linear predictive control'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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Journal articles on the topic "Non-linear predictive control"

1

Willis, M. J., M. T. Tham, G. A. Montague, and A. J. Morris. "Non-Linear Predictive Control." IFAC Proceedings Volumes 24, no. 1 (1991): 69–74. http://dx.doi.org/10.1016/s1474-6670(17)51298-7.

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Valluri, Sairam, Masoud Soroush, and Masoud Nikravesh. "Shortest-prediction-horizon non-linear model-predictive control." Chemical Engineering Science 53, no. 2 (1998): 273–92. http://dx.doi.org/10.1016/s0009-2509(97)00284-4.

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Kouvaritakis, B., M. Cannon, and J. A. Rossiter. "Non-linear model based predictive control." International Journal of Control 72, no. 10 (1999): 919–28. http://dx.doi.org/10.1080/002071799220650.

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Wang, Bo, Muhammad Shahzad, Xianglin Zhu, Khalil Ur Rehman, and Saad Uddin. "A Non-linear Model Predictive Control Based on Grey-Wolf Optimization Using Least-Square Support Vector Machine for Product Concentration Control in l-Lysine Fermentation." Sensors 20, no. 11 (2020): 3335. http://dx.doi.org/10.3390/s20113335.

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l-Lysine is produced by a complex non-linear fermentation process. A non-linear model predictive control (NMPC) scheme is proposed to control product concentration in real time for enhancing production. However, product concentration cannot be directly measured in real time. Least-square support vector machine (LSSVM) is used to predict product concentration in real time. Grey-Wolf Optimization (GWO) algorithm is used to optimize the key model parameters (penalty factor and kernel width) of LSSVM for increasing its prediction accuracy (GWO-LSSVM). The proposed optimal prediction model is used

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Ouyang, H., G. P. Liu, D. Rees, and W. Hu. "Predictive control of networked non-linear control systems." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 221, no. 3 (2007): 453–63. http://dx.doi.org/10.1243/09596518jsce271.

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Hu, X. B., and W. H. Chen. "Model predictive control for non-linear missiles." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 221, no. 8 (2007): 1077–89. http://dx.doi.org/10.1243/09596518jsce394.

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Cannon, M., B. Kouvaritakis, Y. I. Lee, and A. C. Brooms. "Efficient non-linear model based predictive control." International Journal of Control 74, no. 4 (2001): 361–72. http://dx.doi.org/10.1080/00207170010010597.

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Zhang, Runfan, Diyi Chen, Wei Yao, Duoduo Ba, and Xiaoyi Ma. "Non-linear fuzzy predictive control of hydroelectric system." IET Generation, Transmission & Distribution 11, no. 8 (2017): 1966–75. http://dx.doi.org/10.1049/iet-gtd.2016.1300.

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Bequette, B. Wayne. "Non-Linear Model Predictive Control: A Personal Retrospective." Canadian Journal of Chemical Engineering 85, no. 4 (2007): 408–15. http://dx.doi.org/10.1002/cjce.5450850403.

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Baffi, G., J. Morris, and E. Martin. "Non-Linear Model Based Predictive Control Through Dynamic Non-Linear Partial Least Squares." Chemical Engineering Research and Design 80, no. 1 (2002): 75–86. http://dx.doi.org/10.1205/026387602753393240.

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Dissertations / Theses on the topic "Non-linear predictive control"

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Townsend, Shane Martin Joseph. "Non-linear model predictive control." Thesis, Queen's University Belfast, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.301061.

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Hong, Tao. "Non-linear predictive control of chemical processes." Thesis, University of Newcastle Upon Tyne, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.245078.

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Korsfeldt, Larsén Alexander. "Non-linear Model Predictive Control for space debris removal missions." Thesis, Luleå tekniska universitet, Rymdteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-72049.

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The rapidly increasing amounts of space debris orbiting Earth is threatening to reach a critical level, where the near-Earth environment becomes so overfilled with junk that many missions simply become unfeasible. Long-term active debris removal operations appear to be a necessity, but due to the scale of the problem this will likely be an expensive affair spanning decades or even centuries. Many of the mission-related costs can be significantly reduced by making use of a smaller spacecraft, such as the rapidly developing CubeSat standard. An issue with this approach is the limited actuation c

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Torabi, Zahra. "Distributed non-cooperative robust economic predictive control for dynamically coupled linear systems." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2022.

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In this thesis, a tube-based Distributed Economic Predictive Control (DEPC) scheme is presented for a group of dynamically coupled linear subsystems. These subsystems are components of a large scale system and control inputs are computed based on optimizing a local economic objective. Each subsystem is interacting with its neighbors by sending its future reference trajectory, at each sampling time. It solves a local optimization problem in parallel, based on the received future reference trajectories of the other subsystems. To ensure recursive feasibility and a performance bound, each subsys

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Owa, Kayode Olayemi. "Non-linear model predictive control strategies for process plants using soft computing approaches." Thesis, University of Plymouth, 2014. http://hdl.handle.net/10026.1/3031.

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The developments of advanced non-linear control strategies have attracted a considerable research interests over the past decades especially in process control. Rather than an absolute reliance on mathematical models of process plants which often brings discrepancies especially owing to design errors and equipment degradation, non-linear models are however required because they provide improved prediction capabilities but they are very difficult to derive. In addition, the derivation of the global optimal solution gets more difficult especially when multivariable and non-linear systems are inv

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Flood, Cecilia. "Real-time Trajectory Optimization for Terrain Following Based on Non-linear Model Predictive Control." Thesis, Linköping University, Department of Electrical Engineering, 2001. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-1136.

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<p>There are occasions when it is preferable that an aircraft flies asclose to the ground as possible. It is difficult for a pilot to predict the topography when he cannot see beyond the next hill, and this makes it hard for him to find the optimal flight trajectory. With the help of a terrain database in the aircraft, the forthcoming topography can be found in advance and a flight trajectory can be calculated in real-time. The main goal is to find an optimal control sequence to be used by the autopilot. The optimization algorithm, which is created for finding the optimal control sequence, has

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Beal, Logan Daniel. "Large-Scale Non-Linear Dynamic Optimization For Combining Applications of Optimal Scheduling and Control." BYU ScholarsArchive, 2018. https://scholarsarchive.byu.edu/etd/7021.

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Optimization has enabled automated applications in chemical manufacturing such as advanced control and scheduling. These applications have demonstrated enormous benefit over the last few decades and continue to be researched and refined. However, these applications have been developed separately with uncoordinated objectives. This dissertation investigates the unification of scheduling and control optimization schemes. The current practice is compared to early-concept, light integrations, and deeper integrations. This quantitative comparison of economic impacts encourages further investigation

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Botha, Stefan. "Hybrid non-linear model predictive control of a run-of-mine ore grinding mill circuit." Diss., University of Pretoria, 2018. http://hdl.handle.net/2263/66915.

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A run-of-mine (ROM) ore milling circuit is primarily used to grind incoming ore containing precious metals to a powder fine enough to liberate the valuable minerals contained therein. The ground ore has a product particle size specification that is set by the downstream separation unit. A ROM ore milling circuit typically consists of a mill, sump and classifier (most commonly a hydrocyclone). These circuits are difficult to control because of unmeasurable process outputs, non-linearities, time delays, large unmeasured disturbances and complex models with modelling uncertainties. The ROM o

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Hoffmann, Kai [Verfasser]. "Non-linear model-based predictive control of a low-temperature gasoline combustion engine / Kai Hoffmann." Düsseldorf : VDI-Verl, 2010. http://d-nb.info/1005312478/34.

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Porfirio, Carlos Roberto. "Implantação de otimizador online acoplado ao controle preditivo (MPC) de uma coluna de Tolueno." Universidade de São Paulo, 2011. http://www.teses.usp.br/teses/disponiveis/3/3137/tde-31052011-164903/.

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O objetivo principal desta tese foi a implantação de uma nova estratégia para a integração da otimização em tempo real (RTO), com o controle preditivo multivariável em uma unidade de processo industrial. A solução proposta pode ser considerada como uma estratégia de uma camada, na qual os problemas de controle e otimização econômica são resolvidos simultaneamente, na mesma camada da estrutura de controle. Supondo que o objetivo econômico a ser maximizado (minimizado) seja uma função côncava (convexa) das entradas e saídas de processo, o controlador MPC com otimização econômica (OMPC) foi obtid

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Books on the topic "Non-linear predictive control"

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

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2

(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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Guay, DeHaan, and Adetola. Robust and Adaptive Model Predictive Control of Non-linear Systems. Institution of Engineering and Technology, 2015. http://dx.doi.org/10.1049/pbce083e.

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Healey, Richard. Interlude: Some Alternative Interpretations. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198714057.003.0007.

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An understanding of quantum theory is manifested by the ability successfully and unproblematically to use it to further the scientific goals of prediction, explanation, and control of natural phenomena. An Interpretation seeks further to formulate or reformulate it as a fundamental theory that provides a self-contained description of the world. I critically review three prominent but radically different Interpretations of quantum theory (Bohmian mechanics, non-linear theories, Everettian quantum mechanics) and give my reasons for rejecting each as a way of understanding quantum theory. These i

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Book chapters on the topic "Non-linear predictive control"

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Sáez, Doris, Aldo Cipriano, and Andrzej W. Ordys. "Non-Linear Predictive Control." In Advances in Industrial Control. Springer London, 2002. http://dx.doi.org/10.1007/978-1-4471-0113-0_3.

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Sunan, Huang, Tan Kok Kiong, and Lee Tong Heng. "Adaptive Predictive Control of a Class of SISO Non-Linear Systems." In Applied Predictive Control. Springer London, 2002. http://dx.doi.org/10.1007/978-1-4471-3725-2_7.

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Metzler, Mathias, Davide Tavernini, Aldo Sorniotti, and Patrick Gruber. "Explicit non-linear model predictive control for vehicle stability control." In Proceedings. Springer Fachmedien Wiesbaden, 2018. http://dx.doi.org/10.1007/978-3-658-22050-1_49.

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Witczak, Marcin, and Piotr Witczak. "Efficient Predictive Fault-Tolerant Control for Non-linear Systems." In Advances in Intelligent Systems and Computing. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-39881-0_5.

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Buciakowski, Mariusz, Marcin Witczak, and Józef Korbicz. "Towards Robust Predictive Control for Non-linear Discrete Time System." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23180-8_13.

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Domek, Stefan. "Switched Fractional State-Space Predictive Control Methods for Non-Linear Fractional Systems." In Lecture Notes in Electrical Engineering. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-17344-9_9.

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Hufnagel, T., C. Reichert, and D. Schramm. "Centralized Non-linear Model Predictive Control of a Redundantly Actuated Parallel Manipulator." In New Trends in Mechanism and Machine Science. Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-4902-3_65.

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Yu, Xinhong, Dongliang Ke, Libin Xu, Wei Chen, Nanzhen Chen, and Fengxiang Wang. "Linear Extended State Observer Based Model Predictive Control for Non-isolated Two-Stage Inverter." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0553-9_18.

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Blasco, Xavier, Miguel Martinez, Juan Senent, and Javier Sanchis. "Generalized predictive control using genetic algorithms (GAGPC). An application to control of a non-linear process with model uncertainty." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/3-540-64582-9_773.

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Stržinar, Žiga, and Igor Škrjanc. "Self-tuned Model-Based Predictive Control Using Evolving Fuzzy Model of a Non-linear Dynamic Process." In Explainable AI and Other Applications of Fuzzy Techniques. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-82099-2_37.

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Conference papers on the topic "Non-linear predictive control"

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Rebello, Carine Menezes, Erbet Almeida Costa, Marcos Pellegrini Ribeiro, Marcio Fontana, Leizer Schnitman, and Idelfonso Bessa dos Reis Nogueira. "Non-Linear Model Predictive Control for Oil Production in Wells Using Electric Submersible Pumps." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.178327.

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The oil production in wells using electric submersible pumps (ESPs) demands precise control of parameters within safety and efficiency constraints to minimise failures, extend equipment lifespan, and reduce costs. This study proposes a non-linear model predictive control (NMPC) system designed for ESP-lifted wells, leveraging pump frequency and choke valve adjustments to maximise production while adhering to operational limits. Tested on a simulated pilot plant using a first-principles model to predict key variables like flow and liquid column height, the NMPC demonstrated offset-free performa

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Sperti, Matteo, Marco Ambrosio, Mauro Martini, Alessandro Navone, Andrea Ostuni, and Marcello Chiaberge. "Non-linear Model Predictive Control for Multi-task GPS-free Autonomous Navigation in Vineyards." In 2024 IEEE 20th International Conference on Automation Science and Engineering (CASE). IEEE, 2024. http://dx.doi.org/10.1109/case59546.2024.10711737.

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Dikarew, Alexej, and Tobias Winkler. "Rotorcraft Guidance with Sampling based Model Predictive Control." In Vertical Flight Society 80th Annual Forum & Technology Display. The Vertical Flight Society, 2024. http://dx.doi.org/10.4050/f-0080-2024-1051.

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A key technology in automation of rotorcraft flight is collision-free guidance. Especially in operations close to the ground, detection and automatic avoidance of ground-based obstacles and aircraft is a demanding task. This paper presents a sampling-based model predictive approach for collision-free guidance of rotorcraft. The method calculates control inputs for the flight controller by predicting the closed-loop dynamics of the rotorcraft for a short time horizon and evaluating the predictions with a cost function, which can take an arbitrary form. The approach implements a simple algorithm

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Al-Assaf, Yousef. "Long Range Non-Linear Predictive Control." In 1993 American Control Conference. IEEE, 1993. http://dx.doi.org/10.23919/acc.1993.4792975.

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Willis, M. J., G. A. Montague, C. Di Massimo, M. T. Tham, and A. J. Morris. "Non-Linear Predictive Control using Optimisation Techniques." In 1991 American Control Conference. IEEE, 1991. http://dx.doi.org/10.23919/acc.1991.4791910.

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Galatsopoulos, Charalampos, Simira Papadopoulou, Chrysovalantou Ziogou, Dimitris Trigkas, Christos Yfoulis, and Spyridon Voutetakis. "Non-Linear Model Predictive Control for Preventing Premature Aging in Battery Energy Storage System." In 2018 UKACC 12th International Conference on Control (CONTROL). IEEE, 2018. http://dx.doi.org/10.1109/control.2018.8516720.

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Del Vecchio, D. "Non-Linear Prediction Horizon Time-Discretization for Model Predictive Control of Linear Sampled-Data Systems." In 2006 IEEE Conference on Computer Aided Control System Design, 2006 IEEE International Conference on Control Applications, 2006 IEEE International Symposium on Intelligent Control. IEEE, 2006. http://dx.doi.org/10.1109/cca.2006.285937.

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Gondhalekar, Ravi, and Jun-ichi Imura. "Non-linear prediction horizon time-discretization for model predictive control of linear sampled-data systems." In 2006 IEEE Conference on Computer Aided Control System Design, 2006 IEEE International Conference on Control Applications, 2006 IEEE International Symposium on Intelligent Control. IEEE, 2006. http://dx.doi.org/10.1109/cacsd-cca-isic.2006.4776713.

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Zhang, Haoran, and Emmanuel Prempain. "Non-linear Model Predictive Control of a Quadruple-Tank Process." In 2022 UKACC 13th International Conference on Control (CONTROL). IEEE, 2022. http://dx.doi.org/10.1109/control55989.2022.9781448.

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van Essen, H. A., and H. Nijmeijer. "Non-linear model predictive control for constrained mobile robots." In 2001 European Control Conference (ECC). IEEE, 2001. http://dx.doi.org/10.23919/ecc.2001.7076072.

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Reports on the topic "Non-linear predictive control"

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Alchanatis, Victor, Stephen W. Searcy, Moshe Meron, W. Lee, G. Y. Li, and A. Ben Porath. Prediction of Nitrogen Stress Using Reflectance Techniques. United States Department of Agriculture, 2001. http://dx.doi.org/10.32747/2001.7580664.bard.

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Commercial agriculture has come under increasing pressure to reduce nitrogen fertilizer inputs in order to minimize potential nonpoint source pollution of ground and surface waters. This has resulted in increased interest in site specific fertilizer management. One way to solve pollution problems would be to determine crop nutrient needs in real time, using remote detection, and regulating fertilizer dispensed by an applicator. By detecting actual plant needs, only the additional nitrogen necessary to optimize production would be supplied. This research aimed to develop techniques for real tim

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An Input Linearized Powertrain Model for the Optimal Control of Hybrid Electric Vehicles. SAE International, 2022. http://dx.doi.org/10.4271/2022-01-0741.

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Models of hybrid powertrains are used to establish the best combination of conventional engine power and electric motor power for the current driving situation. The model is characteristic for having two control inputs and one output constraint: the total torque should be equal to the torque requested by the driver. To eliminate the constraint, several alternative formulations are used, considering engine power or motor power or even the ratio between them as a single control input. From this input and the constraint, both power levels can be deduced. There are different popular choices for th

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