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Journal articles on the topic 'Model-based predictive control (MBPC)'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 January 2023

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1

Bot, Karol, Inoussa Laouali, António Ruano, and Maria da Graça Ruano. "Home Energy Management Systems with Branch-and-Bound Model-Based Predictive Control Techniques." Energies 14, no. 18 (September 16, 2021): 5852. http://dx.doi.org/10.3390/en14185852.

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At a global level, buildings constitute one of the most significant energy-consuming sectors. Current energy policies in the EU and the U.S. emphasize that buildings, particularly those in the residential sector, should employ renewable energy and storage and efficiently control the total energy system. In this work, we propose a Home Energy Management System (HEMS) by employing a Model-Based Predictive Control (MBPC) framework, implemented using a Branch-and-Bound (BAB) algorithm. We discuss the selection of different parameters, such as time-step, to employ prediction and control horizons and the effect of the weather in the system performance. We compare the economic performance of the proposed approach against a real PV-battery system existing in a household equipped with several IoT devices, concluding that savings larger than 30% can be obtained, whether on sunny or cloudy days. To the best of our knowledge, these are excellent values compared with existing solutions available in the literature.

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2

Sendoya, Diego Fernando. "¿Qué es el Control Predictivo y Hacia Dónde se Proyecta?" Publicaciones e Investigación 7 (June 2, 2013): 53. http://dx.doi.org/10.22490/25394088.1106.

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<p align="left">El control predictivo basado en modelo (<em>Model Based Predictive Control – MBPC o simplemente MPC</em>) es una metodologia de control que hace uso del modelo del proceso para predecir las salidas futuras de la planta y con base en ello optimizar las acciones de control futuras. De hecho, el control predictivo no se puede considerar como una estrategia de control independiente sino, que por el contrario, integra toda una familia de metodos de control tales como, el control optimo, el control de procesos con tiempos muertos, el control de procesos multivariables, etc. Esto ha permitido que el control predictivo haya tenido un desarrollo importante tanto en la comunidad cientifica y academica, como en el sector industrial.</p>

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3

Merabti, Halim, and Khaled Belarbi. "Accelerated micro particle swarm optimization for the solution of nonlinear model predictive control." World Journal of Engineering 14, no. 6 (December 4, 2017): 509–21. http://dx.doi.org/10.1108/wje-01-2017-0004.

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Purpose Rapid solution methods are still a challenge for difficult optimization problems among them those arising in nonlinear model predictive control. The particle swarm optimization algorithm has shown its potential for the solution of some problems with an acceptable computation time. In this paper, we use an accelerated version of PSO for the solution of simple and multiobjective nonlinear MBPC for unmanned vehicles (mobile robots and quadcopter) for tracking trajectories and obstacle avoidance. The AµPSO-NMPC was applied to control a LEGO mobile robot for the tracking of a trajectory without and with obstacles avoidance one. Design/methodology/approach The accelerated PSO and the NMPC are used to control unmanned vehicles for tracking trajectories and obstacle avoidance. Findings The results of the experiments are very promising and show that AµPSO can be considered as an alternative to the classical solution methods. Originality/value The computation time is less than 0.02 ms using an Intel Core i7 with 8GB of RAM.

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Mahmoudi, Abdelkader, Imed Jlassi, Antonio J. Marques Cardoso, and Khaled Yahia. "Model Free Predictive Current Control Based on a Grey Wolf Optimizer for Synchronous Reluctance Motors." Electronics 11, no. 24 (December 13, 2022): 4166. http://dx.doi.org/10.3390/electronics11244166.

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A Model-based predictive current control (MBPCC) has recently become a powerful advanced control technology in industrial drives. However, MBPCC relies on the knowledge of the system model and parameters, being, therefore, very sensitive to parameters errors. In the case of the synchronous reluctance motor (SynRM), where the parameters vary due to its ferromagnetic structure and nonlinear magnetic properties, MBPCC performance would suffer significantly. Accordingly, in this paper, a Grey Wolf Optimizer based model-free predictive current control (GW-MFPCC) of SynRM is proposed, to skip all the effects of the model dependency and parameters uncertainty. The proposed method predicts the stator current through tracking the minimum cost function using the grey wolf optimizer. The proposed GW-MFPCC scheme is compared to MBPCC, and its effectiveness is evaluated and confirmed by experimental results.

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5

Donaisky, Emerson, Gustavo H. C. Oliveira, and Nathan Mendes. "Algoritmos PMV-MBPC para conforto térmico em edificações e aplicação em uma célula-teste." Sba: Controle & Automação Sociedade Brasileira de Automatica 21, no. 1 (February 2010): 01–13. http://dx.doi.org/10.1590/s0103-17592010000100001.

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Este artigo trata o problema de controle de conforto térmico para ocupantes de edificações. Conforto térmico e um conceito de difícil definição e neste trabalho, utilisa-ze índice PMV (Predicted Mean Vote) para sua avaliação. Através deste índice, duas estratégias de controle preditivo caracterizadas por ter restrições terminais, denominadas aqui de PMV-MBPC (PMV Model Based Predictive Controller), são apresentadas. Na primeira estratégia, a gestão do conforto termico é realizada através da geração de sinais de referêsencia para o controlador, que otimiza o valor de PMV dentro de uma zona térmica da edificação. Na segunda, o modelo de PMV está incluso nos cálculos de previsão do controlador, gerando um modelo não-linear com estrutura Wiener. Resultados relacionados com a garantia de estabilidade do sistema em malha fechada são propostos. Neste contexto, um ambiente para testes (célula-teste) de sistemas de controle é descrita e a primeira abordagem é então implementada em tempo real neste ambiente usando um aquecedor a óleo. Resultados experimentais ilustram o desempenho do sistema controle para conforto térmico. Adicionalmente, resultados de simulação, conduzidos com dados climáticos horários, ilustram também o desempenho dos algoritmos de controle.

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6

Wang, Yaqi, and Zhigang Liu. "Suppression Research Regarding Low-Frequency Oscillation in the Vehicle-Grid Coupling System Using Model-Based Predictive Current Control." Energies 11, no. 7 (July 10, 2018): 1803. http://dx.doi.org/10.3390/en11071803.

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Recently, low-frequency oscillation (LFO) has occurred many times in high-speed railways and has led to traction blockades. Some of the literature has found that the stability of the vehicle-grid coupling system could be improved by optimizing the control strategy of the traction line-side converter (LSC) to some extent. In this paper, a model-based predictive current control (MBPCC) approach based on continuous control set in the dq reference frame for the traction LSC for electric multiple units (EMUs) is proposed. First, the mathematical predictive model of one traction LSC is deduced by discretizing the state equation on the alternating current (AC) side. Then, the optimal control variables are calculated by solving the performance function, which involves the difference between the predicted and reference value of the current, as well as the variations of the control voltage. Finally, combined with bipolar sinusoidal pulse width modulation (SPWM), the whole control algorithm based on MBPCC is formed. The simulation models of EMUs’ dual traction LSCs are built in MATLAB/SIMULINK to verify the superior dynamic and static performance, by comparing them with traditional transient direct current control (TDCC). A whole dSPACE semi-physical platform is established to demonstrate the feasibility and effectiveness of MBPCC in real applications. In addition, the simulations of multi-EMUs accessed in the vehicle-grid coupling system are carried out to verify the suppressing effect on LFO. Finally, to find the impact of external parameters (the equivalent leakage inductance of vehicle transformer, the distance to the power supply, and load resistance) on MBPCC’s performance, the sensitivity analysis of these parameters is performed. Results indicate that these three parameters have a tiny impact on the proposed method but a significant influence on the performance of TDCC. Both oscillation pattern and oscillation peak under TDCC can be easily influenced when these parameters change.

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7

Amiryousefi, Ali, Ville Kinnula, and Jing Tang. "Bayes in Wonderland! Predictive Supervised Classification Inference Hits Unpredictability." Mathematics 10, no. 5 (March 5, 2022): 828. http://dx.doi.org/10.3390/math10050828.

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The marginal Bayesian predictive classifiers (mBpc), as opposed to the simultaneous Bayesian predictive classifiers (sBpc), handle each data separately and, hence, tacitly assume the independence of the observations. Due to saturation in learning of generative model parameters, the adverse effect of this false assumption on the accuracy of mBpc tends to wear out in the face of an increasing amount of training data, guaranteeing the convergence of these two classifiers under the de Finetti type of exchangeability. This result, however, is far from trivial for the sequences generated under Partition Exchangeability (PE), where even umpteen amount of training data does not rule out the possibility of an unobserved outcome (Wonderland!). We provide a computational scheme that allows the generation of the sequences under PE. Based on that, with controlled increase of the training data, we show the convergence of the sBpc and mBpc. This underlies the use of simpler yet computationally more efficient marginal classifiers instead of simultaneous. We also provide a parameter estimation of the generative model giving rise to the partition exchangeable sequence as well as a testing paradigm for the equality of this parameter across different samples. The package for Bayesian predictive supervised classifications, parameter estimation and hypothesis testing of the Ewens sampling formula generative model is deposited on CRAN as PEkit package.

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8

Roset *, Bas, and Henk Nijmeijer. "Observer-based model predictive control." International Journal of Control 77, no. 17 (November 20, 2004): 1452–62. http://dx.doi.org/10.1080/00207170412331326855.

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9

Vassileva, Svetla, and Štefan Kozák. "NN Model-Based Predictive Control." IFAC Proceedings Volumes 33, no. 13 (June 2000): 495–500. http://dx.doi.org/10.1016/s1474-6670(17)37239-7.

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10

Roset, Bas, and Henk Nijmeijer. "Observer based model predictive control." IFAC Proceedings Volumes 37, no. 13 (September 2004): 769–74. http://dx.doi.org/10.1016/s1474-6670(17)31318-6.

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11

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

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12

Ross, R. "Revolutionising model-based predictive control." Computing and Control Engineering 14, no. 6 (December 1, 2003): 26–29. http://dx.doi.org/10.1049/cce:20030605.

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13

Onat, Ahmet, A. Teoman Naskali, and Emrah Parlakay. "Model Based Predictive Networked Control Systems." IFAC Proceedings Volumes 41, no. 2 (2008): 13000–13005. http://dx.doi.org/10.3182/20080706-5-kr-1001.02198.

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14

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

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15

Kalra, Lokesh, and Chriatos Georgakis. "Reference system based model predictive control." IFAC Proceedings Volumes 29, no. 1 (June 1996): 5923–28. http://dx.doi.org/10.1016/s1474-6670(17)58629-2.

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16

Gerksic, Samo, Dani Juricic, Stanko Strmcnik, and Drago Matko. "Wiener model based nonlinear predictive control." International Journal of Systems Science 31, no. 2 (January 2000): 189–202. http://dx.doi.org/10.1080/002077200291307.

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17

Baumann, Julian, Dara D. Torkzadeh, Axel Ramstein, Uwe Kiencke, and Thomas Schlegl. "Model-Based Predictive Anti-Jerk Control." IFAC Proceedings Volumes 37, no. 22 (April 2004): 53–58. http://dx.doi.org/10.1016/s1474-6670(17)30321-x.

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18

Cluett, W. R., and E. Goberdhansingh. "Autotuning for model-based predictive control." Automatica 26, no. 4 (July 1990): 691–97. http://dx.doi.org/10.1016/0005-1098(90)90046-k.

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19

Núñez, Alfredo, Doris Sáez, Simon Oblak, and Igor Škrjanc. "Fuzzy-model-based hybrid predictive control." ISA Transactions 48, no. 1 (January 2009): 24–31. http://dx.doi.org/10.1016/j.isatra.2008.10.007.

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20

Kon, Jun-ichiro, and Yosiyuki Yamashita. "Model Predictive Control Based on ARXModels." KAGAKU KOGAKU RONBUNSHU 36, no. 4 (2010): 394–404. http://dx.doi.org/10.1252/kakoronbunshu.36.394.

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21

SAINT-DONAT, JEAN, NAVEEN BHAT, and THOMAS J. McAVOY. "Neural net based model predictive control." International Journal of Control 54, no. 6 (December 1991): 1453–68. http://dx.doi.org/10.1080/00207179108934221.

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22

Baumann, Julian, Dara D. Torkzadeh, Axel Ramstein, Uwe Kiencke, and Thomas Schlegl. "Model-based predictive anti-jerk control." Control Engineering Practice 14, no. 3 (March 2006): 259–66. http://dx.doi.org/10.1016/j.conengprac.2005.03.026.

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23

Wills, Adrian G., and William P. Heath. "Barrier function based model predictive control." Automatica 40, no. 8 (August 2004): 1415–22. http://dx.doi.org/10.1016/j.automatica.2004.03.002.

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24

Ulusoy, Alphan, Ahmet Onat, and Ozgur Gurbuz. "Wireless Model Based Predictive Networked Control System." IFAC Proceedings Volumes 42, no. 3 (2009): 40–47. http://dx.doi.org/10.3182/20090520-3-kr-3006.00007.

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25

OTOSHI, Tatsuya, Yuichi OHSITA, Masayuki MURATA, Yousuke TAKAHASHI, Noriaki KAMIYAMA, Keisuke ISHIBASHI, Kohei SHIOMOTO, and Tomoaki HASHIMOTO. "Traffic Engineering Based on Model Predictive Control." IEICE Transactions on Communications E98.B, no. 6 (2015): 996–1007. http://dx.doi.org/10.1587/transcom.e98.b.996.

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26

O' Brien, M., S. E. Pinto Castillo, and R. Katebi. "MODEL BASED PREDICTIVE CONTROL FOR WASTEWATER APPLICATIONS." IFAC Proceedings Volumes 38, no. 1 (2005): 167–72. http://dx.doi.org/10.3182/20050703-6-cz-1902.02199.

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27

Ichikawa, A., A. E. Vardy, and J. M. B. Brown. "Model-based predictive control using genetic algorithms." Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy 215, no. 5 (January 2001): 623–38. http://dx.doi.org/10.1243/0957650011538857.

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28

SOTNIKOVA, MARGARITA. "PLASMA STABILIZATION BASED ON MODEL PREDICTIVE CONTROL." International Journal of Modern Physics A 24, no. 05 (February 20, 2009): 999–1008. http://dx.doi.org/10.1142/s0217751x09044450.

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The nonlinear model predictive control algorithms for plasma current and shape stabilization are proposed. Such algorithms are quite suitable for the situations when the plant to be controlled has essentially nonlinear dynamics. Besides that, predictive model based control algorithms allow to take into account a lot of requirements and constraints involved both on the controlled and manipulated variables. The significant drawback of the algorithms is that they require a lot of time to compute control input at each sampling instant. In this paper the model predictive control algorithms are demonstrated by the example of plasma vertical stabilization for ITER-FEAT tokamak. The tuning of parameters of algorithms is performed in order to decrease computational load.

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29

Linke, Hartmut, and Eckhard Arnold. "MODEL BASED PREDICTIVE CONTROL OF RIVER RESERVOIRS." IFAC Proceedings Volumes 35, no. 1 (2002): 367–72. http://dx.doi.org/10.3182/20020721-6-es-1901.00549.

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30

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 (January 2001): 361–72. http://dx.doi.org/10.1080/00207170010010597.

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31

Wang, D. H., and C. B. Soh. "Adaptive neural model-based decentralized predictive control." International Journal of Systems Science 31, no. 1 (January 2000): 119–30. http://dx.doi.org/10.1080/002077200291523.

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32

Di Ruscio, David, and Bjarne Foss. "On State Space Model Based Predictive Control." IFAC Proceedings Volumes 31, no. 11 (June 1998): 301–6. http://dx.doi.org/10.1016/s1474-6670(17)44945-7.

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33

Norquay, Sandra J., Ahmet Palazoglu, and JoséA Romagnoli. "Model predictive control based on Wiener models." Chemical Engineering Science 53, no. 1 (January 1998): 75–84. http://dx.doi.org/10.1016/s0009-2509(97)00195-4.

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34

Bayer, Florian A., Matthias A. Müller, and Frank Allgöwer. "Tube-based robust economic model predictive control." Journal of Process Control 24, no. 8 (August 2014): 1237–46. http://dx.doi.org/10.1016/j.jprocont.2014.06.006.

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35

Richalet, J. "Industrial applications of model based predictive control." Automatica 29, no. 5 (September 1993): 1251–74. http://dx.doi.org/10.1016/0005-1098(93)90049-y.

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36

Tesi, A., S. Abu el Ata-Doss, L. Delineau, J. L. Estival, and H. Butler. "Model Based Predictive Control of Exotic Systems." IFAC Proceedings Volumes 23, no. 8 (August 1990): 249–56. http://dx.doi.org/10.1016/s1474-6670(17)51924-2.

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37

Zhong, Guo Qi, and Zhi Yuan Liu. "Cooperation-Based Explicit Distributed Model Predictive Control." Applied Mechanics and Materials 380-384 (August 2013): 707–11. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.707.

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in this paper, an explicit distributed model predictive control method for a class of linear system with control information coupling by means of multi-parametric programming is established. In order to get close to the optimal performance of centralized MPC, the method is based on cooperation, which solving weighted global cost instead of local ones. The method is employed on distillation column control problem to verify the efficiency.

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38

Jurado, F., and M. Ortega. "Model Based Predictive Control of Fuel Cells." Electric Power Components and Systems 34, no. 5 (May 2006): 587–602. http://dx.doi.org/10.1080/15325000500352121.

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39

Chang, Sehyun, and Timothy J. Gordon. "Model-based predictive control of vehicle dynamics." International Journal of Vehicle Autonomous Systems 5, no. 1/2 (2007): 3. http://dx.doi.org/10.1504/ijvas.2007.014945.

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40

Tippett, Michael James, and Jie Bao. "Distributed model predictive control based on dissipativity." AIChE Journal 59, no. 3 (June 27, 2012): 787–804. http://dx.doi.org/10.1002/aic.13868.

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41

Hanssen, Kristian G., and Bjarne Foss. "Scenario Based Implicit Dual Model Predictive Control." IFAC-PapersOnLine 48, no. 23 (2015): 416–21. http://dx.doi.org/10.1016/j.ifacol.2015.11.314.

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42

Mayne, D. Q., E. C. Kerrigan, E. J. van Wyk, and P. Falugi. "Tube-based robust nonlinear model predictive control." International Journal of Robust and Nonlinear Control 21, no. 11 (May 25, 2011): 1341–53. http://dx.doi.org/10.1002/rnc.1758.

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43

Kouvaritakis, B., W. Wang, and Y. I. Lee. "Observers in nonlinear model-based predictive control." International Journal of Robust and Nonlinear Control 10, no. 10 (2000): 749–61. http://dx.doi.org/10.1002/1099-1239(200008)10:10<749::aid-rnc509>3.0.co;2-t.

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44

et al., Aslam. "PLC based model predictive control for industrial process control." International Journal of ADVANCED AND APPLIED SCIENCES 4, no. 6 (June 2017): 63–71. http://dx.doi.org/10.21833/ijaas.2017.06.009.

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45

Tettamanti, Tamás, and István Varga. "Distributed traffic control system based on model predictive control." Periodica Polytechnica Civil Engineering 54, no. 1 (2010): 3. http://dx.doi.org/10.3311/pp.ci.2010-1.01.

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46

Pua, ZY, AW Hermansson, and CH Lim. "ANN based Multi Model Predictive Control for pH-Control." IOP Conference Series: Materials Science and Engineering 1257, no. 1 (October 1, 2022): 012035. http://dx.doi.org/10.1088/1757-899x/1257/1/012035.

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Abstract Artificial neural network (ANN) has many uses when non-linear behaviour is modelled. Here we are training a feedforward ANN that will mimic the behaviour of a Robust Model Predictive Controller (RMPC) for use in pH control. The training dataset were generated from running multiple tests on RMPC for different requirements and cases of pH-control. The training data focused on the control-inputs relating to the other process inputs. The training algorithm used in this neural network is Levenberg-Marquardt algorithm which is the most widely use algorithm in current machine learning industry. This neural network was trained by using the deep learning toolbox in Matlab®. Eight different cases is presented: four is for deploying neural network purpose, while the other four is for verification purpose. The result shows good control as long as the ANN-controller has been given a similar input and there are no multiplicity in the process input data.

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47

Mutha, Rajendra K., William R. Cluett, and Alexander Penlidis. "Nonlinear model-based predictive control of control nonaffine systems." Automatica 33, no. 5 (May 1997): 907–13. http://dx.doi.org/10.1016/s0005-1098(96)00220-8.

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48

Kim, Yeonsoo, Tae Hoon Oh, Taekyoon Park, and Jong Min Lee. "Backstepping control integrated with Lyapunov-based model predictive control." Journal of Process Control 73 (January 2019): 137–46. http://dx.doi.org/10.1016/j.jprocont.2018.12.007.

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49

Mahmood, Maaz, and Prashant Mhaskar. "Constrained control Lyapunov function based model predictive control design." International Journal of Robust and Nonlinear Control 24, no. 2 (August 31, 2012): 374–88. http://dx.doi.org/10.1002/rnc.2896.

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50

Gerkšič, Samo, Đani Juričić, and Ton J. J. Van Den Boom. "Nonlinear Model-Based Predictive Control Using a Wiener Model." IFAC Proceedings Volumes 30, no. 27 (October 1997): 201–6. http://dx.doi.org/10.1016/s1474-6670(17)41181-5.

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