Relevant bibliographies by topics / Predictive control – Computer simulation

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Predictive control – Computer simulation'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Predictive control – Computer simulation"

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Tian, Zi De. "Algorithm and Implementation of Smith Predictive Control." Applied Mechanics and Materials 687-691 (November 2014): 60–63. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.60.

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With invariable time delay theoretically,Smith predictor is deeply studied,the fact that the Smith predictor depends on the math model of the system is found ,and so it is very difficult to control the time-varying delay system well. Therefore,it is necessary to take an effective method--using PID controller with the digital Smith predictor,and studying its control algorithm,and doing the simulation in the lab of micro-computer control. Simulation results have proved the efficiency of the algorithm and the validity.
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Tong, Ming Yu, Di Jian Xu, and Jin Liang Shi. "Furnace Temperature Control and Simulation." Applied Mechanics and Materials 556-562 (May 2014): 2492–95. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.2492.

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with the rapid development of science and technology, in all fields of temperature control system of the precision, stability and other requirements of increasingly high, the control system is the myriads of changes. Computer measurement and control technology, the traditional electronic measuring changed dramatically in the principle, function, accuracy and automation degree, degree of automation of the scientific experiment and application engineering to improve. Temperature control key lies in the two aspects of temperature measurement and control. Temperature measurement is based on temper
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Kontes, Georgios, Georgios Giannakis, Víctor Sánchez, et al. "Simulation-Based Evaluation and Optimization of Control Strategies in Buildings." Energies 11, no. 12 (2018): 3376. http://dx.doi.org/10.3390/en11123376.

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Over the last several years, a great amount of research work has been focused on the development of model predictive control techniques for the indoor climate control of buildings, but, despite the promising results, this technology is still not adopted by the industry. One of the main reasons for this is the increased cost associated with the development and calibration (or identification) of mathematical models of special structure used for predicting future states of the building. We propose a methodology to overcome this obstacle by replacing these hand-engineered mathematical models with
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Brüdigam, Tim, Johannes Teutsch, Dirk Wollherr, Marion Leibold, and Martin Buss. "Probabilistic model predictive control for extended prediction horizons." at - Automatisierungstechnik 69, no. 9 (2021): 759–70. http://dx.doi.org/10.1515/auto-2021-0025.

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Abstract Detailed prediction models with robust constraints and small sampling times in Model Predictive Control yield conservative behavior and large computational effort, especially for longer prediction horizons. Here, we extend and combine previous Model Predictive Control methods that account for prediction uncertainty and reduce computational complexity. The proposed method uses robust constraints on a detailed model for short-term predictions, while probabilistic constraints are employed on a simplified model with increased sampling time for long-term predictions. The underlying methods
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Lu, Jialiang, Guanrong Chen, and Hao Ying. "Predictive fuzzy PID control: theory, design and simulation." Information Sciences 137, no. 1-4 (2001): 157–87. http://dx.doi.org/10.1016/s0020-0255(01)00119-0.

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Tang, Zhi Jie, Qing Bo He, Shu Ai Wang, and Jia Li Shen. "An Improved Generalized Predictive Control for AUV Yaw." Advanced Materials Research 490-495 (March 2012): 1709–13. http://dx.doi.org/10.4028/www.scientific.net/amr.490-495.1709.

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In this paper, a new design for underwater vehicle yaw control is presented for nonlinear, large delay, time-varying systems. The horizontal stability controller combines generalized predictive control with FuzzyPID control method. The predictive function is introduced into the tradition FuzzyPID control through optimizing the performance index function of GPC. Computer simulation is provided for verification, and the results show this new controller has a better performance
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Yu, Kaijiang, Xiaozhuo Xu, Qing Liang, et al. "Model Predictive Control for Connected Hybrid Electric Vehicles." Mathematical Problems in Engineering 2015 (2015): 1–15. http://dx.doi.org/10.1155/2015/318025.

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This paper presents a new model predictive control system for connected hybrid electric vehicles to improve fuel economy. The new features of this study are as follows. First, the battery charge and discharge profile and the driving velocity profile are simultaneously optimized. One is energy management for HEV forPbatt; the other is for the energy consumption minimizing problem of acc control of two vehicles. Second, a system for connected hybrid electric vehicles has been developed considering varying drag coefficients and the road gradients. Third, the fuel model of a typical hybrid electri
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Li, Hongwei, Kaide Ren, Shuaibing Li, and Haiying Dong. "Adaptive Multi-Model Switching Predictive Active Power Control Scheme for Wind Generator System." Energies 13, no. 6 (2020): 1329. http://dx.doi.org/10.3390/en13061329.

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To deal with the randomness and uncertainty of the wind power generation process, this paper proposes the use of the clustering method to complement the multi-model predictive control algorithm for active power control. Firstly, the fuzzy clustering algorithm is adopted to classify actual measured data; then, the forgetting factor recursive least square method is used to establish the multi-model of the system as the prediction model. Secondly, the model predictive controller is designed to use the measured wind speed as disturbance, the pitch angle as the control variable, and the active powe
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Lin, Fen, Yuke Chen, Youqun Zhao, and Shaobo Wang. "Path tracking of autonomous vehicle based on adaptive model predictive control." International Journal of Advanced Robotic Systems 16, no. 5 (2019): 172988141988008. http://dx.doi.org/10.1177/1729881419880089.

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In most cases, a vehicle works in a complex environment, with working conditions changing frequently. For most model predictive tracking controllers, however, the impacts of some important working conditions, such as speed and road conditions, are not concerned. In this regard, an adaptive model predictive controller is proposed, which improves tracking accuracy and stability compared with general model predictive controllers. First, the proposed controller utilizes the recursive least square algorithm to estimate tire cornering stiffness and road friction coefficient online. Then, the estimat
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Lee, Leng-Feng, and Brian R. Umberger. "Generating optimal control simulations of musculoskeletal movement using OpenSim and MATLAB." PeerJ 4 (January 26, 2016): e1638. http://dx.doi.org/10.7717/peerj.1638.

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Computer modeling, simulation and optimization are powerful tools that have seen increased use in biomechanics research. Dynamic optimizations can be categorized as either data-tracking or predictive problems. The data-tracking approach has been used extensively to address human movement problems of clinical relevance. The predictive approach also holds great promise, but has seen limited use in clinical applications. Enhanced software tools would facilitate the application of predictive musculoskeletal simulations to clinically-relevant research. The open-source software OpenSim provides tool
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Dissertations / Theses on the topic "Predictive control – Computer simulation"

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Silva, 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.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.<br>Includes bibliographical references (p. 52-56).<br>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 cont
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Farley, Mark Harrison. "Predicting machining accuracy and duration of an NC mill by computer simulation." Thesis, Georgia Institute of Technology, 1990. http://hdl.handle.net/1853/16499.

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Walker, Jens. "A motion cueing model for mining and forestry simulator platforms based on Model Predictive Control." Thesis, Umeå universitet, Institutionen för fysik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-98685.

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Oryx Simulations produce simulators for mining and forestry machinery used for educational and promotional purposes. The simulators use motion platforms to reflect how the vehicle moves within the simulator. This platform tilts and accelerates the driver in order to enhance the experience. Previously a classical washout filter algorithm has been used to control the platform which leaves something to be desired regarding how well it reflects the vehicles movement, how easy it is to tune and how it handles the limits of the platform. This thesis aims to produce a model that accurately reflects a
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Zuerlein, Scott A. "Predicting the medical management requirements of large scale mass casualty events using computer simulation." [Tampa, Fla] : University of South Florida, 2009. http://purl.fcla.edu/usf/dc/et/SFE0002836.

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Fraher, Richard Louis. "Optimizing roof control using probabilistic techniques in roof failure prediction." Thesis, This resource online, 1992. http://scholar.lib.vt.edu/theses/available/etd-10062009-020200/.

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Crosby, Garth. "Predictive performance modeling and simulation." FIU Digital Commons, 2001. http://digitalcommons.fiu.edu/etd/2671.

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The purpose of this thesis was to create and simulate a model of an existing Campus network with a view to predict future performance. This thesis also suggests ways in which such a network can be optimized. Such simulation and modeling can be referred to as "Predictive Performance Modeling'. In this research a model of Florida International University (University Park campus) High Speed Network was created. Simulation of the model was carried out and an ATM Backbone Analysis was done. The results obtained were compared with corresponding results obtained by network performance monitoring and
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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.

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<p> Model 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
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MeVay, Alex C. H. (Alex Craige Haviland) 1979. "Predictive comparators with adaptive control." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/29654.

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Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2002.<br>Includes bibliographical references (p. 72).<br>A linear predictor and adaptive control loop are added to a conventional comparator to greatly reduce the delay. A linear predictor feeds an estimated future signal to the comparator to compensate for the comparator's internal delay. On a cycle-by-cycle basis, an adaptive controller adjusts the comparator's bias current to null the error. Emphasis is placed on low power consumption, including the development of a linear predictor
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Weber, Thomas Alois 1969. "Constrained predictive control for corporate policy." Thesis, Massachusetts Institute of Technology, 1997. http://hdl.handle.net/1721.1/10219.

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Mohajerin, Nima. "Identification and Predictive Control Using RecurrentNeural Networks." Thesis, Örebro universitet, Institutionen för naturvetenskap och teknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-21762.

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In this thesis, a special class of Recurrent Neural Networks (RNN) is employed for system identification and predictive control of time dependent systems. Fundamental architectures and learning algorithms of RNNs are studied upon which a generalized architecture over a class of state-space represented networks is proposed and formulated. Levenberg-Marquardt (LM) learning algorithm is derived for this architecture and a number of enhancements are introduced. Furthermore, using this recurrent neural network as a system identifier, a Model Predictive Controller (MPC) is established which solves t
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Books on the topic "Predictive control – Computer simulation"

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Sunan, Huang. Applied Predictive Control. Springer London, 2002.

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Camacho, E. F. Model predictive control. 2nd ed. Springer, 2004.

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1962-, Bordons C., ed. Model predictive control. Springer, 1999.

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Camacho, E. F. Model predictive control. Springer, 2003.

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McMillan, Gregory K. Models unleashed: Virtual plant and model predictive control applications : a pocket guide. ISA, 2004.

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Kolpakov, Vasiliy. Economic and mathematical and econometric modeling: Computer workshop. INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/24417.

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The textbook presents mathematical research methods and models of economic objects and processes designed for the analysis and prediction of economic factors and develop control solutions as in the deterministic conditions, and in conditions of some uncertainty, and dynamics. Each Chapter of the book consists of a theoretical framework, discussed in detail several examples and tasks for independent work. As workbench simulation uses standard office the program Excel and Mathcad. Tutorial focused on independent performance of students individual tasks on disciplines "Economic-mathematical metho
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Mass, Clifford. A next-generation land surface model for the prediction of pavement temperature. Washington State Dept. of Transportation, 2003.

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Myszkowski, K. Efficient and predictive realistic image synthesis. Oficyna Wydawn. Politechniki Warszawskiej, 2001.

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Dabrowski, Jarek. Predictive Simulation of Semiconductor Processing: Status and Challenges. Springer Berlin Heidelberg, 2004.

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M, Allbeck Jan, and Badler Norman I, eds. Virtual crowds: Methods, simulation, and control. Morgan & Claypool Publishers, 2008.

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Book chapters on the topic "Predictive control – Computer simulation"

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Huang, Mingsheng, and Huawen Zhang. "Simulation of Ship Path Prediction and Remote Control Based on UE4 Next Generation Rendering." In Communications in Computer and Information Science. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-33-4601-7_15.

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Takács, Gergely, and Boris Rohal’-Ilkiv. "Simulation Study of Model Predictive Vibration Control." In Model Predictive Vibration Control. Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2333-0_11.

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Palmieri, Giovanni, Osvaldo Barbarisi, Stefano Scala, and Luigi Glielmo. "An Integrated LTV-MPC Lateral Vehicle Dynamics Control: Simulation Results." In Automotive Model Predictive Control. Springer London, 2010. http://dx.doi.org/10.1007/978-1-84996-071-7_15.

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Gáspár, Péter, and Balázs Németh. "Simulation and Validation of Predictive Cruise Control." In Advances in Industrial Control. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04116-8_11.

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Wu, Jianguo, and Minrui Fei. "The Networked Control Systems Based on Predictive Functional Control." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/978-3-540-37275-2_136.

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Deng, YiBo, Hua Zhang, and GuoHong Ma. "Predictive Control Simulation on Binocular Visual Servo Seam Tracking System." In Electrical Engineering and Control. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21765-4_57.

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Deligianni, Fani, Adrian Chung, and Guang Zhong. "Predictive Camera Tracking for Bronchoscope Simulation with CONDensation." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11566465_112.

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Funk, James E., and Dennis R. Dinger. "PPC Computer Programs." In Predictive Process Control of Crowded Particulate Suspensions. Springer US, 1994. http://dx.doi.org/10.1007/978-1-4615-3118-0_36.

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Li, Xi, Xiao-wei Fu, Guang-yi Cao, and Xin-jian Zhu. "Fuzzy Predictive Control Based on PEMFC Stack." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-28648-6_23.

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Prinz, Astrid A., and Scott L. Hooper. "Computer Simulation-Power and Peril." In Neurobiology of Motor Control. John Wiley & Sons, Inc., 2017. http://dx.doi.org/10.1002/9781118873397.ch5.

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Conference papers on the topic "Predictive control – Computer simulation"

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Liu, Chunling, Jizi Li, Junfeng Wang, and Yangjie Tian. "Supply Chain Simulation with Switching Adaptive Model Predictive Control Methodology." In 2016 International Conference on Sensor Network and Computer Engineering. Atlantis Press, 2016. http://dx.doi.org/10.2991/icsnce-16.2016.124.

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Zhou, Hui, Zhihong Xiao, and Qichen Zhu. "Simulation of power system based on grey predictive excitation control." In 2011 24th IEEE Canadian Conference on Electrical and Computer Engineering (CCECE). IEEE, 2011. http://dx.doi.org/10.1109/ccece.2011.6030709.

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Fahmy, Ismail M., Ahmed M. Kamel, Ahmed F. Nassar, and Khaled A. El-Metwally. "Real-time simulation of applying model predictive control on an industrial evaporation process." In 2015 Tenth International Conference on Computer Engineering & Systems (ICCES). IEEE, 2015. http://dx.doi.org/10.1109/icces.2015.7393016.

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Zhang, Fengchao, Jianyu Chen, Wei Wang, Hong Han, Xin Li, and Juanjuan Zhang. "Optimization of Gait Assistance Pattern for Charcot-Marie-Tooth Patients Based on Forward Predictive Simulation." In 2021 International Conference on Computer, Control and Robotics (ICCCR). IEEE, 2021. http://dx.doi.org/10.1109/icccr49711.2021.9349388.

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Abu-Ayyad, Ma’moun, Lakshamirinyan Chinta Venkateswararao, and Rickey Dubay. "Nonlinear Generalized Predictive Control Approach for Challenging Processes." In ASME 2009 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. ASMEDC, 2009. http://dx.doi.org/10.1115/smasis2009-1212.

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This paper presents the implementation of the fundamental concept of the infinite modeling methodology to the generalized predictive control (GPC) algorithm. This method was termed as infinite modeling generalized predictive control (IMGPC) which uses the nonlinear characteristics of the process such as the process gain and time constant to recalculate the dynamic matrix every sampling instant. Computer simulations were performed on nonlinear plants with different degrees of nonlinearity demonstrating that the infinite modeling approach is readily implemented providing improved control perform
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Caruntu, Constantin Florin, Doina Onu, Florin Catalin Braescu, and Corneliu Lazar. "Model Predictive Control for Real-Time Simulation of a Network-Controlled Vehicle Drivetrain." In 2011 2nd Eastern European Regional Conference on the Engineering of Computer Based Systems (ECBS-EERC 2011). IEEE, 2011. http://dx.doi.org/10.1109/ecbs-eerc.2011.25.

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Martin, Mathieu, Chris Patton, John Schmitt, and Sourabh V. Apte. "Direct Simulation Based Model-Predictive Control of Flow Maldistribution in Parallel Microchannels." In ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/detc2009-87537.

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The goal of this investigation is to develop a simulation-based control strategy to eliminate flow-maldistribution in parallel microchannels. An accurate simulation of fluid flow through parallel microchannels is achieved by utilizing a fictitious domain representation of immersed objects, such as microvalves and bubbles. System identification techniques are employed to produce a lower dimensional model that captures the essential dynamics of the full nonlinear flow, in terms of a relationship between the valve angles and the exit flow rate for each channel. The resulting linear model is incor
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Su, Chunyuan, Hujun Ling, Yinbo Liu, Jinghua Li, and Zhongyang Yuan. "Simulation about applying generalized predictive control algorithm based on improved Elman neural network to combustion system of circulating fluidized bed boiler." In 5th International Conference on Advanced Computer Control. WIT Press, 2014. http://dx.doi.org/10.2495/icacc130451.

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Wang, Zeyu, and Molin Zhao. "Finite-Set Model Predictive Control for Eight-Switch Three-Phase NPC Converter with DC-Link Voltages Fluctuation Suppression." In 2018 International Conference on Computer Modeling, Simulation and Algorithm (CMSA 2018). Atlantis Press, 2018. http://dx.doi.org/10.2991/cmsa-18.2018.20.

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Lin, He, Wan Zhou, Cheng Li, Liao Xingzhi, and Han Jinchuan. "Simulink Simulation of Single Neuron PID and Smith Predictive Control Based on the s-Function." In 2013 Third International Conference on Instrumentation, Measurement, Computer, Communication and Control (IMCCC). IEEE, 2013. http://dx.doi.org/10.1109/imccc.2013.345.

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Reports on the topic "Predictive control – Computer simulation"

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Middlebrooks, Sam E., and Brian J. Stankiewicz. Toward the Development of a Predictive Computer Model of Decision Making During Uncertainty for Use in Simulations of U.S. Army Command and Control System. Defense Technical Information Center, 2006. http://dx.doi.org/10.21236/ada443462.

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Kettering, B., and P. Van Arsdall. Integrated computer control system startup simulation. Office of Scientific and Technical Information (OSTI), 1998. http://dx.doi.org/10.2172/8307.

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Kettering, B., and P. Van Arsdall. Integrated computer control system status monitor simulation. Office of Scientific and Technical Information (OSTI), 1998. http://dx.doi.org/10.2172/8308.

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Van Arsdall, P., and C. E. Annese. Integrated computer control system countdown status messages simulation. Office of Scientific and Technical Information (OSTI), 1998. http://dx.doi.org/10.2172/8047.

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Middlebrooks, Sam E., John P. Jones, and Patrick H. Henry. The Compass Paradigm for the Systematic Evaluation of U.S. Army Command and Control Systems Using Neural Network and Discrete Event Computer Simulation. Defense Technical Information Center, 2005. http://dx.doi.org/10.21236/ada450646.

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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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Bobashev, Georgiy, John Holloway, Eric Solano, and Boris Gutkin. A Control Theory Model of Smoking. RTI Press, 2017. http://dx.doi.org/10.3768/rtipress.2017.op.0040.1706.

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We present a heuristic control theory model that describes smoking under restricted and unrestricted access to cigarettes. The model is based on the allostasis theory and uses a formal representation of a multiscale opponent process. The model simulates smoking behavior of an individual and produces both short-term (“loading up” after not smoking for a while) and long-term smoking patterns (e.g., gradual transition from a few cigarettes to one pack a day). By introducing a formal representation of withdrawal- and craving-like processes, the model produces gradual increases over time in withdra
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Shaver, Greg, and Miles Droege. Develop and Deploy a Safe Truck Platoon Testing Protocol for the Purdue ARPA-E Project in Indiana. Purdue University, 2021. http://dx.doi.org/10.5703/1288284317314.

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Hilly terrain poses challenges to truck platoons using fixed set speed cruise control. Driving the front truck efficiently on hilly terrain improves both trucks fuel economies and improves gap maintenance between the trucks. An experimentally-validated simulation model was used to show fuel savings for the platoon of 12.3% when the front truck uses long horizon predictive cruise control (LH-PCC), 8.7% when the front truck uses flexible set speed cruise control, and only 1.2% when the front truck uses fixed set speed cruise control. Purdue, Peloton, and Cummins have jointly configured two Peter
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