Academic literature on the topic 'Predictive simulation model'
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Journal articles on the topic "Predictive simulation model"
El Hachimi, Mohamed, Abdelhakim Ballouk, and AbdNaceur Baghdad. "Fuzzy Model Predictive Controller for Artificial Pancreas." International Journal of Power Electronics and Drive Systems (IJPEDS) 9, no. 3 (September 1, 2018): 1178. http://dx.doi.org/10.11591/ijpeds.v9.i3.pp1178-1185.
Full textLi, Chao, Gang Huang, Zhongyang Hao, and Cheng Liu. "A Motion Control Simulation Based on Predictive Model." Journal of Physics: Conference Series 2333, no. 1 (August 1, 2022): 012012. http://dx.doi.org/10.1088/1742-6596/2333/1/012012.
Full textLee, Robin L., Brendon A. Bradley, Peter J. Stafford, Robert W. Graves, and Adrian Rodriguez-Marek. "Hybrid broadband ground motion simulation validation of small magnitude earthquakes in Canterbury, New Zealand." Earthquake Spectra 36, no. 2 (February 2, 2020): 673–99. http://dx.doi.org/10.1177/8755293019891718.
Full textBrüdigam, Tim, Johannes Teutsch, Dirk Wollherr, Marion Leibold, and Martin Buss. "Probabilistic model predictive control for extended prediction horizons." at - Automatisierungstechnik 69, no. 9 (September 1, 2021): 759–70. http://dx.doi.org/10.1515/auto-2021-0025.
Full textGiwa, Abdulwahab, Abel Adekanmi Adeyi, and Saidat Olanipekun Giwa. "Control of a Reactive Distillation Process Using Model Predictive Control Toolbox of MATLAB." International Journal of Engineering Research in Africa 30 (May 2017): 167–80. http://dx.doi.org/10.4028/www.scientific.net/jera.30.167.
Full textKour, Ravdeep, Adithya Thaduri, and Ramin Karim. "Predictive model for multistage cyber-attack simulation." International Journal of System Assurance Engineering and Management 11, no. 3 (February 3, 2020): 600–613. http://dx.doi.org/10.1007/s13198-020-00952-5.
Full textStenhaug, Benjamin A., and Benjamin W. Domingue. "Predictive Fit Metrics for Item Response Models." Applied Psychological Measurement 46, no. 2 (February 13, 2022): 136–55. http://dx.doi.org/10.1177/01466216211066603.
Full textOlofsen, Erik, and Albert Dahan. "Using Akaike's information theoretic criterion in mixed-effects modeling of pharmacokinetic data: a simulation study." F1000Research 2 (May 28, 2014): 71. http://dx.doi.org/10.12688/f1000research.2-71.v2.
Full textOlofsen, Erik, and Albert Dahan. "Using Akaike's information theoretic criterion in mixed-effects modeling of pharmacokinetic data: a simulation study." F1000Research 2 (July 27, 2015): 71. http://dx.doi.org/10.12688/f1000research.2-71.v3.
Full textBecker, Dennis, Vincent Bremer, Burkhardt Funk, Mark Hoogendoorn, Artur Rocha, and Heleen Riper. "Evaluation of a temporal causal model for predicting the mood of clients in an online therapy." Evidence Based Mental Health 23, no. 1 (February 2020): 27–33. http://dx.doi.org/10.1136/ebmental-2019-300135.
Full textDissertations / Theses on the topic "Predictive simulation model"
Shah, Nirali. "Simulation of Model Predictive Control using Dynamic Matrix Control algorithm." Thesis, California State University, Long Beach, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=1604872.
Full textModel Predictive Control has emerged as a very powerful technology in the area of process control for three decades. The objective of this work was to develop Dynamic Matrix Control Algorithm, one of the most widely used Model Predictive Control Algorithms using MATLAB and simulate it for a real world Single Input Single Output system. This thesis focuses on the impacts and importance of the tuning parameters of Dynamic Matrix Control along with an overview of the general Model Predictive Control strategy. The tuning of the Dynamic Matrix Controller was done by trial and error based on the knowledge of the simulated system under consideration and the control strategy. The Control Signal computed was then implemented on the system to study its effect on the system output using a discrete transfer function model. The results of the tuned controller were observed to be similar to the other tuning methods discussed in the literature.
Wei, Zhouping, University of Western Sydney, and of Mechatronic Computer and Electrical Engineering School. "Model predictive control of a robot using neural networks." THESIS_XXX_MCEE_Wei_Z.xml, 1999. http://handle.uws.edu.au:8081/1959.7/323.
Full textMaster of Engineering (Hons)
Riley, Matthew E. "Quantification of Model-Form, Predictive, and Parametric Uncertainties in Simulation-Based Design." Wright State University / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=wright1314895435.
Full textSilva, Marco Jorge Tome da. "Simulation of human motion data using short-horizon model-predictive control." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/43041.
Full textIncludes bibliographical references (p. 52-56).
Many data-driven animation techniques are capable of producing high quality motions of human characters. Few techniques, however, are capable of generating motions that are consistent with physically simulated environments. Physically simulated characters, in contrast, are automatically consistent with the environment, but their motions are often unnatural because they are difficult to control. We present a model-predictive controller that yields natural motions by guiding simulated humans toward real motion data. During simulation, the predictive component of the controller solves a quadratic program to compute the forces for a short window of time into the future. These forces are then applied by a low-gain proportional-derivative component, which makes minor adjustments until the next planning cycle. The controller is fast enough for interactive systems such as games and training simulations. It requires no precomputation and little manual tuning. The controller is resilient to mismatches between the character dynamics and the input motion, which allows it to track motion capture data even where the real dynamics are not known precisely. The same principled formulation can generate natural walks, runs, and jumps in a number of different physically simulated surroundings.
by Marco da Silva.
S.M.
Toschi, Alessandro. "Integration of Model Predictive Control for autonomous racing." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2022.
Find full textAbdul-Jalal, Rifqi I. "Engine thermal management with model predictive control." Thesis, Loughborough University, 2016. https://dspace.lboro.ac.uk/2134/24274.
Full textVara-Cadillo, Gabriel. "Autonomous Car Overtake Using Model Predictive Control." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-293818.
Full textAutonoma fordon har lyckats locka till sig mer populäritet under de senaste åren. En autonom bil har möjligheten att manövrera på ett säkert och effektivt sätt. Detta i kombination med ett fokus att öka vägsäkerheten har lagt större press på att implementera reglersystem för omkörningar. Modell prediktiv reglering (MPC) är användbar för den kan hantera linjära bivillkor och fungerar till autonomon körning. Ett reglersystem är implementerat i Python och testades på sin omkörningförmåga med olika hastigheter, avstånd och begynnelse hastigheter. Implementationen utformades med bivillkor som att det autonoma fordonet inte ska krocka med ett annat fordon eller köra utanför vägen i en omkörning. Resultaten visar att det gick att köra om på ett säkert sätt med vissa förutsättningar. MPC algoritmen har visat sig användbar men svår att optimera.
Kandidatexjobb i elektroteknik 2020, KTH, Stockholm
Sheth, Katha Janak. "Model predictive control for adaptive digital human modeling." Thesis, University of Iowa, 2010. https://ir.uiowa.edu/etd/884.
Full textHorii, M. Michael. "A Predictive Model for Multi-Band Optical Tracking System (MBOTS) Performance." International Foundation for Telemetering, 2013. http://hdl.handle.net/10150/579658.
Full textIn the wake of sequestration, Test and Evaluation (T&E) groups across the U.S. are quickly learning to make do with less. For Department of Defense ranges and test facility bases in particular, the timing of sequestration could not be worse. Aging optical tracking systems are in dire need of replacement. What's more, the increasingly challenging missions of today require advanced technology, flexibility, and agility to support an ever-widening spectrum of scenarios, including short-range (0 − 5 km) imaging of launch events, long-range (50 km+) imaging of debris fields, directed energy testing, high-speed tracking, and look-down coverage of ground test scenarios, to name just a few. There is a pressing need for optical tracking systems that can be operated on a limited budget with minimal resources, staff, and maintenance, while simultaneously increasing throughput and data quality. Here we present a mathematical error model to predict system performance. We compare model predictions to site-acceptance test results collected from a pair of multi-band optical tracking systems (MBOTS) fielded at White Sands Missile Range. A radar serves as a point of reference to gauge system results. The calibration data and the triangulation solutions obtained during testing provide a characterization of system performance. The results suggest that the optical tracking system error model adequately predicts system performance, thereby supporting pre-mission analysis and conserving scarce resources for innovation and development of robust solutions. Along the way, we illustrate some methods of time-space-position information (TSPI) data analysis, define metrics for assessing system accuracy, and enumerate error sources impacting measurements. We conclude by describing technical challenges ahead and identifying a path forward.
Zsolt, Pap Levente. "Model Predictive Control of Electric Drives -Design, Simulation and Implementation of PMSM Torque Control." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-240365.
Full textDen här uppsatsen handlar om designen och implementeringen av en motorstyrning för en permanen- magnetiserad synkronmotor, med syfte att ersätta standardmotorstyrningsenheten i KTH Formula Students tävlingsbil. Implementationen av styralgoritmen testades experimentellt tillsammans med en prototyptillverkad frekvensomriktare i labbmiljö. Regleralgoritmer för field oriented control och finite control set model predictive control implementerades och testades i simuleringsmiljö. Den senare algoritmen visade sig prestera bättre i form av lägre vridmomentsoscillationer trots lägre switch-frekvens men den kräver samtidigt mer beräkningskraft. Övertonsinnehållet (THD) i fasströmmarna som funktion av switchfrekvensen undersöktes för de båda regleralgoritmerna, algoritmen för model predictive control gav lägre THD vid lägre frekvenser (1-20 kHz). Simuleringsresultaten användes för att motivera valet av komponenter till frekvensomriktaren. Regleralgoritmen för field oriented control implementerades och testades experimentellt med hjälp av ett utvecklingskort (TMS320F28335) från Texas Instruments. SPI-kommunikation användes för att konfigurera drivkretsana samt för att utläsa felkoder. Experimentalla tester som utfördes på låg spänningsnivå visade att strömmen till lasten var sinusformad. Mätning av verkningsgrad och provning tillsammans med motorn på en högre spänningsnivå gick inte att geno av att de snabba switchförloppen i kiselkarbidtransistorerna störde ut motorstyrningen.
Books on the topic "Predictive simulation model"
1962-, Bordons C., ed. Model predictive control. Berlin: Springer, 1999.
Find full textCamacho, E. F. Model predictive control. London: Springer, 2003.
Find full textCamacho, E. F. Model predictive control. 2nd ed. New York: Springer, 2004.
Find full textRichards, Thomas. A predictive model of Aboriginal archaeological site distribution in the Otway Range. [Melbourne]: Aboriginal Affairs Victoria, 1998.
Find full textBousfield, Wayne E. Application of predictive model to forecast Douglas-fir tussock moth defoliation. Missoula, Mont: USDA Forest Service, Northern Region, 1986.
Find full textMcMillan, Gregory K. Models unleashed: Virtual plant and model predictive control applications : a pocket guide. Research Triangle Park, NC: ISA, 2004.
Find full textBradley, John E. An archaeological survey and predictive model of selected areas of Utah's Cisco Desert. Salt Lake City, Utah: Bureau of Land Management, Utah State Office, 1986.
Find full textBlack, Kevin D. The Castle Valley archaeological project: An inventory and predictive model of selected tracts. Salt Lake City, Utah: Utah State Office, Bureau of Land Management, 1986.
Find full textBlack, Kevin D. The Castle Valley archaeological project: An inventory and predictive model of selected tracts. Salt Lake City, Utah: Utah State Office, Bureau of Land Management, 1986.
Find full textBradley, John E. An archaeological survey and predictive model of selected areas of Utah's Cisco Desert. Salt Lake City, Utah: Utah State Office, Bureau of Land Management, 1986.
Find full textBook chapters on the topic "Predictive simulation model"
Takács, Gergely, and Boris Rohal’-Ilkiv. "Simulation Study of Model Predictive Vibration Control." In Model Predictive Vibration Control, 391–425. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2333-0_11.
Full textPalmieri, Giovanni, Osvaldo Barbarisi, Stefano Scala, and Luigi Glielmo. "An Integrated LTV-MPC Lateral Vehicle Dynamics Control: Simulation Results." In Automotive Model Predictive Control, 231–55. London: Springer London, 2010. http://dx.doi.org/10.1007/978-1-84996-071-7_15.
Full textWüthrich, Mario V., and Michael Merz. "Predictive Modeling and Forecast Evaluation." In Springer Actuarial, 75–110. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-12409-9_4.
Full textKaramouzas, Ioannis, Peter Heil, Pascal van Beek, and Mark H. Overmars. "A Predictive Collision Avoidance Model for Pedestrian Simulation." In Motion in Games, 41–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10347-6_4.
Full textPhan, Minh Q., and Seyed Mahdi B. Azad. "Model Predictive Q-Learning (MPQ-L) for Bilinear Systems." In Modeling, Simulation and Optimization of Complex Processes HPSC 2018, 97–115. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-55240-4_5.
Full textTymchuk, Sergii, Sergii Shendryk, Vira Shendryk, Ivan Abramenko, and Anastasiia Kazlauskaite. "The Methodology of Obtaining Power Consumption Fuzzy Predictive Model for Enterprises." In Advances in Design, Simulation and Manufacturing III, 210–19. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-50794-7_21.
Full textBrown, Arthur A., Bonnie R. Antoun, Michael L. Chiesa, Stephen B. Margolis, Devin O’Connor, Jason M. Simmons, Douglas J. Bammann, Chris San Marchi, and Nancy Y. C. Yang. "Predictive Simulation of a Validation Forging Using a Recrystallization Model." In Time Dependent Constitutive Behavior and Fracture/Failure Processes, Volume 3, 51–56. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-9794-4_9.
Full textGinhoux, Romuald, Jacques A. Gangloff, Michel F. de Mathelin, Luc Soler, Jöel Leroy, and Jacques Marescaux. "Model Predictive Control for Cancellation of Repetitive Organ Motions in Robotized Laparoscopic Surgery." In Surgery Simulation and Soft Tissue Modeling, 353–65. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-45015-7_34.
Full textLuo, Zhixiang, Shanbi Wei, Yi Chai, Yanxing Liu, and Xiuling Sun. "Simulation of Wind Farm Scheduling Algorithm Based on Predictive Model Control." In Lecture Notes in Electrical Engineering, 581–91. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6499-9_56.
Full textBernardeschi, Cinzia, Pierpaolo Dini, Andrea Domenici, Ayoub Mouhagir, Maurizio Palmieri, Sergio Saponara, Tanguy Sassolas, and Lilia Zaourar. "Co-simulation of a Model Predictive Control System for Automotive Applications." In Lecture Notes in Computer Science, 204–20. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-12429-7_15.
Full textConference papers on the topic "Predictive simulation model"
HERZOG, Simon, Dennis ATABAY, Johannes JUNGWIRTH, and Vesna MIKULOVIC. "Self-adapting Building Models For Model Predictive Control." In 2017 Building Simulation Conference. IBPSA, 2013. http://dx.doi.org/10.26868/25222708.2013.2298.
Full textHonc, Daniel, and Frantisek Dusek. "State-Space Constrained Model Predictive Control." In 27th Conference on Modelling and Simulation. ECMS, 2013. http://dx.doi.org/10.7148/2013-0441.
Full textErfani, Arash, Xingji Yu, Tuule Mall Kull, Peder Bacher, and Tohid Jafarinejad. "Analysis of the impact of predictive models on the quality of the Model Predictive Control for an experimental building." In 2021 Building Simulation Conference. KU Leuven, 2021. http://dx.doi.org/10.26868/25222708.2021.30566.
Full textLai, C. Y., C. Xiang, and T. H. Lee. "Identification and Control of Piecewise Affine Systems using Multiple Models and Model Predictive Control." In Modelling and Simulation. Calgary,AB,Canada: ACTAPRESS, 2010. http://dx.doi.org/10.2316/p.2010.697-009.
Full textSamek, David. "Elman Neural Networks In Model Predictive Control." In 23rd European Conference on Modelling and Simulation. ECMS, 2009. http://dx.doi.org/10.7148/2009-0577-0581.
Full textGoussous, Faisal, Rajankumar Bhatt, Soura Dasgupta, and Karim Abdel-Malek. "Model Predictive Control for Human Motion Simulation." In Digital Human Modeling for Design and Engineering Conference and Exhibition. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2009. http://dx.doi.org/10.4271/2009-01-2306.
Full textRusar, Lukas, Adam Krhovjak, Stanislav Talas, and Vladimir Bobal. "State-Space Predictive Control Of Inverted Pendulum Model." In 31st Conference on Modelling and Simulation. ECMS, 2017. http://dx.doi.org/10.7148/2017-0384.
Full textNagpal, Himashu, Florin Capitanescu, and Per Heiselberg. "Model predictive control framework for operation of smart sustainable buildings." In 2021 Building Simulation Conference. KU Leuven, 2021. http://dx.doi.org/10.26868/25222708.2021.31111.
Full textMattar, Ebrahim A., and Khaled H. Al Mutib. "Synthesizing Fuzzy Based Model Predictive Controller." In 2011 Third International Conference on Computational Intelligence, Modelling and Simulation (CIMSiM). IEEE, 2011. http://dx.doi.org/10.1109/cimsim.2011.29.
Full textYang, Tao, Fisayo Caleb Sangogboye, Krzysztof Arendt, Konstantin Filonenko, and Jonathan Dallaire. "The impact of occupancy prediction accuracy on the performance of model predictive control (MPC) in buildings." In 2021 Building Simulation Conference. KU Leuven, 2021. http://dx.doi.org/10.26868/25222708.2021.30571.
Full textReports on the topic "Predictive simulation model"
Bays, Samuel E., and David L. Chichester. TREAT HODOSCOPE – A Predictive Simulation Model. Office of Scientific and Technical Information (OSTI), September 2017. http://dx.doi.org/10.2172/1408774.
Full textOberkampf, William Louis, Timothy Guy Trucano, and Martin M. Pilch. Predictive Capability Maturity Model for computational modeling and simulation. Office of Scientific and Technical Information (OSTI), October 2007. http://dx.doi.org/10.2172/976951.
Full textNishimura, Masatsugu, Yoshitaka Tezuka, Enrico Picotti, Mattia Bruschetta, Francesco Ambrogi, and Toru Yoshii. Study of Rider Model for Motorcycle Racing Simulation. SAE International, January 2020. http://dx.doi.org/10.4271/2019-32-0572.
Full textNichols, Will, Stephanie Tomusiak, and Ryan Nell. Predictive Flow Simulation with the P2R Model for the Composite Analysis Base Case. Office of Scientific and Technical Information (OSTI), September 2020. http://dx.doi.org/10.2172/1668408.
Full textTomusiak, Stephanie, Hai Pham, and Trevor Budge. Predictive Flow Simulation with the P2R Model for the Composite Analysis Base Case. Office of Scientific and Technical Information (OSTI), March 2022. http://dx.doi.org/10.2172/1855946.
Full textKohler, Christian. Simulation of complex glazing products; from optical data measurements to model based predictive controls. Office of Scientific and Technical Information (OSTI), April 2012. http://dx.doi.org/10.2172/1171808.
Full textTomusiak, S., and William Nichols. Predictive Contaminant Transport Simulation with the P2R Model for the Composite Analysis Base Case. Office of Scientific and Technical Information (OSTI), October 2020. http://dx.doi.org/10.2172/1706735.
Full textTomusiak, Stephanie, Hai Pham, and Trevor Budge. Predictive Contaminant Transport Simulation with the P2R Model for the Composite Analysis Inventory Sensitivity Case. Office of Scientific and Technical Information (OSTI), March 2022. http://dx.doi.org/10.2172/1856027.
Full textPham, Hai, Stephanie Tomusiak, and Trevor Budge. Predictive Contaminant Transport Simulation with the P2R Model for the Composite Analysis Recharge Sensitivity Case. Office of Scientific and Technical Information (OSTI), March 2022. http://dx.doi.org/10.2172/1856046.
Full textNichols, Will, and S. Tomusiak. Predictive Contaminant Transport Simulation with P2R Model for the Composite Analysis Limited Source Sensitivity Case. Office of Scientific and Technical Information (OSTI), April 2022. http://dx.doi.org/10.2172/1862348.
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