Relevant bibliographies by topics / Supervisory model predictive control

Academic literature on the topic 'Supervisory model predictive control'

Author: Grafiati
Published: 4 June 2021
Last updated: 7 February 2022

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Journal articles on the topic "Supervisory model predictive control"

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Tatjewski, Piotr. "Supervisory predictive control and on-line set-point optimization." International Journal of Applied Mathematics and Computer Science 20, no. 3 (September 1, 2010): 483–95. http://dx.doi.org/10.2478/v10006-010-0035-1.

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Supervisory predictive control and on-line set-point optimizationThe subject of this paper is to discuss selected effective known and novel structures for advanced process control and optimization. The role and techniques of model-based predictive control (MPC) in a supervisory (advanced) control layer are first shortly discussed. The emphasis is put on algorithm efficiency for nonlinear processes and on treating uncertainty in process models, with two solutions presented: the structure of nonlinear prediction and successive linearizations for nonlinear control, and a novel algorithm based on fast model selection to cope with process uncertainty. Issues of cooperation between MPC algorithms and on-line steady-state set-point optimization are next discussed, including integrated approaches. Finally, a recently developed two-purpose supervisory predictive set-point optimizer is discussed, designed to perform simultaneously two goals: economic optimization and constraints handling for the underlying unconstrained direct controllers.
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Jin, Jionghua, Huairui Guo, and Shiyu Zhou. "Statistical Process Control Based Supervisory Generalized Predictive Control of Thin Film Deposition Processes." Journal of Manufacturing Science and Engineering 128, no. 1 (December 15, 2004): 315–25. http://dx.doi.org/10.1115/1.2114912.

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This paper presents a supervisory generalized predictive control (GPC) by combining GPC with statistical process control (SPC) for the control of the thin film deposition process. In the supervised GPC, the deposition process is described as an ARMAX model for each production run and GPC is applied to the in situ thickness-sensing data for thickness control. Supervisory strategies, developed from SPC techniques, are used to monitor process changes and estimate the disturbance magnitudes during production. Based on the SPC monitoring results, different supervisory strategies are used to revise the disturbance models and the control law in the GPC to achieve a satisfactory control performance. A case study is provided to demonstrate the developed methodology.
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Meyer, Kristian, Thomas Bisgaard, Jakob K. Huusom, and Jens Abildskov. "Supervisory Model Predictive Control of the Heat Integrated Distillation Column." IFAC-PapersOnLine 50, no. 1 (July 2017): 7375–80. http://dx.doi.org/10.1016/j.ifacol.2017.08.1506.

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Kobayashi, Takahiro, and Tetsuji Tani. "Application of Cooperative Control to Petroleum Plants Using Fuzzy Supervisory Control and Model Predictive Multi-variable Control." Journal of Advanced Computational Intelligence and Intelligent Informatics 5, no. 6 (November 20, 2001): 333–37. http://dx.doi.org/10.20965/jaciii.2001.p0333.

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This paper describes hierarchical control with fuzzy supervisory control and model predictive multivariable control (MPC) in a petroleum plant. MPC is effective in time delay, interference, and handling constraints. Fuzzy logic controllers are effective for plants with large time delay and non-linearity. Our proposed hierarchical control combines their advantages. Fuzzy supervisory control, which determines set points for MPC, consists of an estimation block and a compensation block. We use a statistical model with multi-regression analysis for the estimation block to estimate parameters of plant operation, and fuzzy logic for the compensation block to correct output of the statistical model. Hierarchical control has been applied to an actual plant in an oil refinery, and showed satisfactory performance.
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Papangelis, Lampros, Marie-Sophie Debry, Patrick Panciatici, and Thierry Van Cutsem. "Coordinated Supervisory Control of Multi-Terminal HVDC Grids: A Model Predictive Control Approach." IEEE Transactions on Power Systems 32, no. 6 (November 2017): 4673–83. http://dx.doi.org/10.1109/tpwrs.2017.2659781.

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Creemers, Falco, Alejandro Ivan Morales Medina, Erjen Lefeber, and Nathan van de Wouw. "Design of a supervisory controller for Cooperative Intersection Control using Model Predictive Control." IFAC-PapersOnLine 51, no. 33 (2018): 74–79. http://dx.doi.org/10.1016/j.ifacol.2018.12.096.

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Li, Su Zhen, Xiang Jie Liu, and Gang Yuan. "Application of Supervisory Predictive Control Based on T-S Model in pH Neutralization Process." Applied Mechanics and Materials 511-512 (February 2014): 867–70. http://dx.doi.org/10.4028/www.scientific.net/amm.511-512.867.

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T-S model is linearized at sampling points into the form of linear time-invariant state space , and using supervisory predictive control and muti-step predictive control strategy, which reduces amount of calculation and improves the control performance. Introduction
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Grosso, J. M., C. Ocampo-Martínez, and V. Puig. "Learning-based tuning of supervisory model predictive control for drinking water networks." Engineering Applications of Artificial Intelligence 26, no. 7 (August 2013): 1741–50. http://dx.doi.org/10.1016/j.engappai.2013.03.003.

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Rosen, C., M. Larsson, U. Jeppson, and Z. Yuan. "A framework for extreme-event control in wastewater treatment." Water Science and Technology 45, no. 4-5 (February 1, 2002): 299–308. http://dx.doi.org/10.2166/wst.2002.0610.

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In this paper an approach to extreme event control in wastewater treatment plant operation by use of automatic supervisory control is discussed. The framework presented is based on the fact that different operational conditions manifest themselves as clusters in a multivariate measurement space. These clusters are identified and linked to specific and corresponding events by use of principal component analysis and fuzzy c-means clustering. A reduced system model is assigned to each type of extreme event and used to calculate appropriate local controller set points. In earlier work we have shown that this approach is applicable to wastewater treatment control using look-up tables to determine current set points. In this work we focus on the automatic determination of appropriate set points by use of steady state and dynamic predictions. The performance of a relatively simple steady-state supervisory controller is compared with that of a model predictive supervisory controller. Also, a look-up table approach is included in the comparison, as it provides a simple and robust alternative to the steady-state and model predictive controllers. The methodology is illustrated in a simulation study.
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Morales-Rodelo, Keidy, Mario Francisco, Hernan Alvarez, Pastora Vega, and Silvana Revollar. "Collaborative Control Applied to BSM1 for Wastewater Treatment Plants." Processes 8, no. 11 (November 16, 2020): 1465. http://dx.doi.org/10.3390/pr8111465.

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This paper describes a design procedure for a collaborative control structure in Plant Wide Control (PWC), taking into account the existing controllable parameters as a novelty in the procedure. The collaborative control structure includes two layers, supervisory and regulatory, which are determined according to the dynamics hierarchy obtained by means of the Hankel matrix. The supervisory layer is determined by the main dynamics of the process and the regulatory layer comprises the secondary dynamics and controllable parameters. The methodology proposed is applied to a wastewater treatment plant, particularly to the Benchmark Simulation Model No 1 (BSM1) for the activated sludge process, comparing the results with the use of a Model Predictive Controller in the supervisory layer. For determining controllable parameters in the BSM1 control, a new specific oxygen mass transfer model in the biological reactor has been developed, separating the kLa volumetric mass transfer coefficient into two controllable parameters, kL and a.
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Dissertations / Theses on the topic "Supervisory model predictive control"

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Huang, Yang, and S3110949@student rmit edu au. "Model Predictive Control of Magnetic Bearing System." RMIT University. Electrical and Computer Engineering, 2007. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20080430.152045.

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Magnetic Bearing Systems have been receiving a great deal of research attention for the past decades. Its inherent nonlinearity and open-loop instability are challenges for controller design. This thesis investigates and designs model predictive control strategy for an experimental Active Magnetic Bearing (AMB) laboratory system. A host-target development environment of real-time control system with hardware in the loop (HIL) is implemented. In this thesis, both continuous and discrete time model predictive controllers are studied. In the first stage, local MPC controllers are applied to control the AMB system; and in the second stage, concept of supervisory controller design is then investigated and implemented. Contributions of the thesis can be summarized as follows; 1. A Discrete time Model Predictive Controller has been developed and applied to the active magnetic bearing system. 2. A Continuous time Model Predictive Controller has been developed and applied to the active magnetic bearing system. 3. A frequency domain identification method using FSF has been applied to pursue model identification with respect to local MPC and magnetic bearing system. 4. A supervisory control strategy has been applied to pursue a two stages model predictive control of active magnetic bearing system.
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Boussemart, Yves 1980. "Predictive models of procedural human supervisory control behavior." Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/79543.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Engineering Systems Division, 2011.
Page 150 blank. Cataloged from PDF version of thesis.
Includes bibliographical references (p. 138-149).
Human supervisory control systems are characterized by the computer-mediated nature of the interactions between one or more operators and a given task. Nuclear power plants, air traffic management and unmanned vehicles operations are examples of such systems. In this context, the role of the operators is typically highly proceduralized due to the time and mission-critical nature of the tasks. Therefore, the ability to continuously monitor operator behavior so as to detect and predict anomalous situations is a critical safeguard for proper system operation. In particular, such models can help support the decision making process of a supervisor of a team of operators by providing alerts when likely anomalous behaviors are detected. By exploiting the operator behavioral patterns which are typically reinforced through standard operating procedures, this thesis proposes a methodology that uses statistical learning techniques in order to detect and predict anomalous operator conditions. More specifically, the proposed methodology relies on hidden Markov models (HMMs) and hidden semi-Markov models (HSMMs) to generate predictive models of unmanned vehicle systems operators. Through the exploration of the resulting HMMs in two distinct single operator scenarios, the methodology presented in this thesis is validated and shown to provide models capable of reliably predicting operator behavior. In addition, the use of HSMMs on the same data scenarios provides the temporal component of the predictions missing from the HMMs. The final step of this work is to examine how the proposed methodology scales to more complex scenarios involving teams of operators. Adopting a holistic team modeling approach, both HMMs and HSMMs are learned based on two team-based data sets. The results show that the HSMMs can provide valuable timing information in the single operator case, whereas HMMs tend to be more robust to increased team complexity. In addition, this thesis discusses the methodological and practical limitations of the proposed approach notably in terms of input data requirements and model complexity. This thesis thus provides theoretical and practical contributions by exploring the validity of using statistical models of operators as the basis for detecting and predicting anomalous conditions.
by Yves Boussemart.
Ph.D.
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Sadr, Faramarz. "Supervisory model predictive control of building integrated renewable and low carbon energy systems." Thesis, Loughborough University, 2012. https://dspace.lboro.ac.uk/2134/9518.

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To reduce fossil fuel consumption and carbon emission in the building sector, renewable and low carbon energy technologies are integrated in building energy systems to supply all or part of the building energy demand. In this research, an optimal supervisory controller is designed to optimize the operational cost and the CO2 emission of the integrated energy systems. For this purpose, the building energy system is defined and its boundary, components (subsystems), inputs and outputs are identified. Then a mathematical model of the components is obtained. For mathematical modelling of the energy system, a unified modelling method is used. With this method, many different building energy systems can be modelled uniformly. Two approaches are used; multi-period optimization and hybrid model predictive control. In both approaches the optimization problem is deterministic, so that at each time step the energy consumption of the building, and the available renewable energy are perfectly predicted for the prediction horizon. The controller is simulated in three different applications. In the first application the controller is used for a system consisting of a micro-combined heat and power system with an auxiliary boiler and a hot water storage tank. In this application the controller reduces the operational cost and CO2 emission by 7.31 percent and 5.19 percent respectively, with respect to the heat led operation. In the second application the controller is used to control a farm electrification system consisting of PV panels, a diesel generator and a battery bank. In this application the operational cost with respect to the common load following strategy is reduced by 3.8 percent. In the third application the controller is used to control a hybrid off-grid power system consisting of PV panels, a battery bank, an electrolyzer, a hydrogen storage tank and a fuel cell. In this application the controller maximizes the total stored energies in the battery bank and the hydrogen storage tank.
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Abraham, Etimbuk. "Adaptive supervisory control scheme for voltage controlled demand response in power systems." Thesis, University of Manchester, 2018. https://www.research.manchester.ac.uk/portal/en/theses/adaptive-supervisory-control-scheme-for-voltage-controlled-demand-response-in-power-systems(3e64537d-52c7-4eb5-87f2-b73fe920b9cb).html.

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Radical changes to present day power systems will lead to power systems with a significant penetration of renewable energy sources and smartness, expressed in an extensive utilization of novel sensors and cyber secure Information and Communication Technology. Although these renewable energy sources prove to contribute to the reduction of CO2 emissions into the environment, its high penetration affects power system dynamic performance as a result of reduced power system inertia as well as less flexibility with regards to dispatching generation to balance future demand. These pose a threat both to the security and stability of future power systems. It is therefore very important to develop new methods through which power system security and stability can be maintained. This research investigated the development of methods through which the contributions of on-load tap changing transformers/transformer clusters could be assessed with the intent of developing real time adaptive voltage controlled demand response schemes for power systems. The development of such a scheme enables more active system components to be involved in the provision of frequency control as an ancillary service and deploys a new frequency control service with low infrastructural investment, bearing in mind that OLTC transformers are already very prevalent in power systems. In this thesis, a novel online adaptive supervisory controller for ensuring optimal dispatch of voltage-controlled demand response resources is developed. This novel controller is designed using the assessment results of OLTC transformer impacts on steady-state frequency and was tested for a variety of scenarios. To achieve the effective performance of the adaptive supervisory controller, the extensive use of statistical techniques for assessing OLTC transformer contributions to voltage controlled demand response is presented. This thesis also includes the use of unsupervised machine learning techniques for power system partitioning and the further use of statistical methods for assessing the contributions of OLTC transformer aggregates.
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Bacic, Marko. "Model predictive control." Thesis, University of Oxford, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.400060.

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Hanger, Martin Bøgseth. "Model Predictive Control Allocation." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for teknisk kybernetikk, 2011. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-13308.

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This thesis developes a control allocation method based on the Model Predictive Control algorithm, to be used on a missile in flight. The resulting Model Predictive Control Allocation (MPCA) method is able to account for actuator constraints and dynamics, setting it aside from most classical methods. A new effector configuration containing two groups of actuators with different dynamic authorities is also proposed. Using this configuration, the MPCA method is compared to the classical methods Linear Programming and Redistributed Pseudoinverse in various flight scenarios, highlighting performance differences aswell as emphasizing applications of the MPCA method. It is found to be superior to the two classical methods in terms of tracking performance and total cost. Nevertheless, some restrictions and weaknesses are revealed, but countermeasures to these are proposed. The newly developed convex optmization solver CVXGEN is utilized successfully in the method evaluation. Providing solve times in milliseconds even for large problems, CVXGEN makes real-time implementations of the MPCA method feasible.
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Sriniwas, Ganti Ravi. "Nonlinear model predictive control." Diss., Georgia Institute of Technology, 1995. http://hdl.handle.net/1853/10267.

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Qi, Kent Zhihua. "Dual-model predictive control." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/nq21621.pdf.

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Couchman, Paul. "Stochastic model predictive control." Thesis, University of Oxford, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.442384.

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Wu, Xingjian. "Stochastic model predictive control." Thesis, University of Oxford, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.497157.

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Books on the topic "Supervisory model predictive control"

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

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

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Kouvaritakis, Basil, and Mark Cannon. Model Predictive Control. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-24853-0.

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Camacho, Eduardo F., and Carlos Bordons. Model Predictive Control. London: Springer London, 1999. http://dx.doi.org/10.1007/978-1-4471-3398-8.

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

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Zhang, Ridong, Anke Xue, and Furong Gao. Model Predictive Control. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-0083-7.

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

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Zheng, Tao. Advanced model predictive control. Rijeka, Croatia: InTech, 2011.

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

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Takács, Gergely, and Boris Rohaľ-Ilkiv. Model Predictive Vibration Control. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2333-0.

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Book chapters on the topic "Supervisory model predictive control"

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Corriou, Jean-Pierre. "Model Predictive Control." In Process Control, 631–77. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61143-3_16.

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Corriou, Jean-Pierre. "Model Predictive Control." In Process Control, 575–615. London: Springer London, 2004. http://dx.doi.org/10.1007/978-1-4471-3848-8_16.

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Kalbasenka, Alex N., Adrie E. M. Huesman, and Herman J. M. Kramer. "Model Predictive Control." In Industrial Crystallization Process Monitoring and Control, 185–201. Weinheim, Germany: Wiley-VCH Verlag GmbH & Co. KGaA, 2012. http://dx.doi.org/10.1002/9783527645206.ch16.

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Wieber, Pierre-Brice. "Model Predictive Control." In Humanoid Robotics: A Reference, 1077–97. Dordrecht: Springer Netherlands, 2018. http://dx.doi.org/10.1007/978-94-007-6046-2_48.

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Rengaswamy, Raghunathan, Babji Srinivasan, and Nirav Pravinbhai Bhatt. "Model Predictive Control." In Process Control Fundamentals, 229–50. First edition. | Boca Raton : CRC Press, 2020.: CRC Press, 2020. http://dx.doi.org/10.1201/9780367433437-8.

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Wieber, Pierre-Brice. "Model Predictive Control." In Humanoid Robotics: A Reference, 1–21. Dordrecht: Springer Netherlands, 2017. http://dx.doi.org/10.1007/978-94-007-7194-9_48-1.

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Frank, Steven A. "Model Predictive Control." In Control Theory Tutorial, 91–94. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91707-8_12.

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Mariéthoz, Sébastien, and Stefan Almér. "Model Predictive Control." In Advances in Industrial Control, 321–54. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2885-4_11.

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Klaučo, Martin, and Michal Kvasnica. "Model Predictive Control." In MPC-Based Reference Governors, 15–34. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-17405-7_3.

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Featherstone, Andrew P., Jeremy G. VanAntwerp, and Richard D. Braatz. "Model Predictive Control." In Advances in Industrial Control, 129–44. London: Springer London, 2000. http://dx.doi.org/10.1007/978-1-4471-0413-1_7.

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Conference papers on the topic "Supervisory model predictive control"

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Ferrara, Antonella, Simona Sacone, and Silvia Siri. "Supervisory Model Predictive Control for freeway traffic systems." In 2013 IEEE 52nd Annual Conference on Decision and Control (CDC). IEEE, 2013. http://dx.doi.org/10.1109/cdc.2013.6759997.

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Falaki, Ali, and Farzad Towhidkhah. "Supervisory model predictive impedance control for human arm movement." In 2012 20th Iranian Conference on Electrical Engineering (ICEE). IEEE, 2012. http://dx.doi.org/10.1109/iraniancee.2012.6292608.

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Tyukov, Anton, Maxim Shcherbakov, Alexander Sokolov, Adriaan Brebels, and Mohammed Al-Gunaid. "Supervisory model predictive on/off control of HVAC systems." In 2017 8th International Conference on Information, Intelligence, Systems & Applications (IISA). IEEE, 2017. http://dx.doi.org/10.1109/iisa.2017.8316434.

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Keadtipod, Pongsorn, and David Banjerdpongchai. "Design of supervisory cascade model predictive control for industrial boilers." In 2016 International Automatic Control Conference (CACS). IEEE, 2016. http://dx.doi.org/10.1109/cacs.2016.7973895.

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Negenborn, R. R., A. G. Beccuti, T. Demiray, S. Leirens, G. Damm, B. De Schutter, and M. Morari. "Supervisory hybrid model predictive control for voltage stability of power networks." In 2007 American Control Conference. IEEE, 2007. http://dx.doi.org/10.1109/acc.2007.4282264.

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Elliott, Matthew S., and Bryan P. Rasmussen. "A model-based predictive supervisory controller for multi-evaporator HVAC systems." In 2009 American Control Conference. IEEE, 2009. http://dx.doi.org/10.1109/acc.2009.5160498.

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Plewe, Kaden E., Amanda D. Smith, and Mingxi Liu. "A Supervisory Model Predictive Control Framework for Dual Temperature Setpoint Optimization." In 2020 American Control Conference (ACC). IEEE, 2020. http://dx.doi.org/10.23919/acc45564.2020.9147308.

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Nejadkazemi, Khatereh, and Ahmad Fakharian. "Pressure control in gas oil pipeline: A supervisory model predictive control approach." In 2016 4th International Conference on Control, Instrumentation, and Automation (ICCIA). IEEE, 2016. http://dx.doi.org/10.1109/icciautom.2016.7483195.

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Elmahdawy, M. Y., M. Abdelgeliel, and Alaa eldin Khalil. "Application of Supervisory Model Predictive Controller in Polymer Extrusion Process." In 2020 4th International Conference on Automation, Control and Robots (ICACR). IEEE, 2020. http://dx.doi.org/10.1109/icacr51161.2020.9265512.

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Bulut, B., M. R. Katebi, and M. A. Johnson. "Industrial application of model based predictive control as a supervisory system." In Proceedings of 2000 American Control Conference (ACC 2000). IEEE, 2000. http://dx.doi.org/10.1109/acc.2000.876924.

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Reports on the topic "Supervisory model predictive control"

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Baum, C. C., K. L. Buescher, V. Hanagandi, R. Jones, and K. Lee. Adaptive model predictive control using neural networks. Office of Scientific and Technical Information (OSTI), September 1994. http://dx.doi.org/10.2172/10178912.

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Castanon, David A., and Jerry M. Wohletz. Model Predictive Control for Dynamic Unreliable Resource Allocation. Fort Belvoir, VA: Defense Technical Information Center, December 2002. http://dx.doi.org/10.21236/ada409519.

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B. Wayne Bequette and Priyadarshi Mahapatra. Model Predictive Control of Integrated Gasification Combined Cycle Power Plants. Office of Scientific and Technical Information (OSTI), August 2010. http://dx.doi.org/10.2172/1026486.

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Li, Dinggen, and Yang Ye. The Control of Air-Fuel Ratio of the Engine Based on Model Predictive Control. Warrendale, PA: SAE International, October 2012. http://dx.doi.org/10.4271/2012-32-0050.

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Ollerenshaw, Douglas, and Mark Costello. Model of Predictive Control of a Direct-Fire Projectile Equipped With Canards. Fort Belvoir, VA: Defense Technical Information Center, March 2005. http://dx.doi.org/10.21236/ada432823.

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Bouffard, Patrick. On-board Model Predictive Control of a Quadrotor Helicopter: Design, Implementation, and Experiments. Fort Belvoir, VA: Defense Technical Information Center, December 2012. http://dx.doi.org/10.21236/ada572108.

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Aswani, Anil, Humberto Gonzalez, S. S. Sastry, and Claire Tomlin. Statistical Results on Filtering and Epi-convergence for Learning-Based Model Predictive Control. Fort Belvoir, VA: Defense Technical Information Center, December 2011. http://dx.doi.org/10.21236/ada558989.

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Haves, Phillip, Brandon Hencey, Francesco Borrell, John Elliot, Yudong Ma, Brian Coffey, Sorin Bengea, and Michael Wetter. Model Predictive Control of HVAC Systems: Implementation and Testing at the University of California, Merced. Office of Scientific and Technical Information (OSTI), June 2010. http://dx.doi.org/10.2172/988177.

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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. Fort Belvoir, VA: Defense Technical Information Center, January 2006. http://dx.doi.org/10.21236/ada443462.

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10

Nishimura, 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.

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Abstract:
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 cope with running conditions that lose contact with the front wheels or rear wheels so-called "endo" and "wheelie", which often occur during running with large acceleration or deceleration assuming a race. For the control inputs to the vehicle, we incorporated the lateral shift of the rider's center of gravity in addition to the normally used inputs such as the steering angle, throttle position, and braking force. We compared the performance of the new model with that of the conventional model under constant radius cornering and straight braking, as well as complex braking and acceleration in a single (hairpin) corner that represented a racing run. The results showed that the new rider model outperformed the conventional model, especially in the wider range of running speed usable for a simulation. In addition, we compared the simulation results for complex braking and acceleration in a single hairpin corner produced by the new model with data from an actual race and verified that the new model was able to accurately simulate the run of actual MotoGP riders.
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