Relevant bibliographies by topics / Expert model predictive controller

Contents

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

Academic literature on the topic 'Expert model predictive controller'

Author: Grafiati
Published: 4 June 2025
Last updated: 15 July 2025

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Journal articles on the topic "Expert model predictive controller"

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Qian, Zheng Zai, Gong Cai Xin, and Jin Niu Tao. "Predictive Control Based on Fuzzy Expert PID Tuning Control." Advanced Materials Research 466-467 (February 2012): 1207–11. http://dx.doi.org/10.4028/www.scientific.net/amr.466-467.1207.

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In decade years, several simple methods for the automatic tuning of PID controllers have been proposed. There have been different approaches to the problem of deriving a PID-like adaptive controller. All of these can be classified into two broad categories: model-based; or expert systems. In this paper a new expert adaptive controller is proposed in which the underlying control law is a PID structure. The design is based on the fuzzy logic and the generalized predictive control theory. The proposed controller can be applied to a large class of systems which is model uncertainty or strong non-l
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Rivas-Perez, SotomayorMoriano, PérezZuñiga, and Soto-Angles. "Real-Time Implementation of an Expert Model Predictive Controller in a Pilot-Scale Reverse Osmosis Plant for Brackish and Seawater Desalination." Applied Sciences 9, no. 14 (2019): 2932. http://dx.doi.org/10.3390/app9142932.

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This article addresses the design and real-time implementation of an expert model predictive controller (Expert MPC) for the control of the brackish and seawater desalination process in a pilot-scale reverse osmosis (RO) plant. This pilot-scale plant is used in order to obtain the optimal operation conditions of the RO desalination process through the implementation of different control strategies, as well as in the training of operators in the new control and management technologies. A dynamical mathematical model of this plant has been developed based on the available field data and system i
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Huang, Zexin, Matthew Best, and James Knowles. "Optimal predictive steering control for autonomous runway exits." Advances in Mechanical Engineering 12, no. 12 (2020): 168781402098086. http://dx.doi.org/10.1177/1687814020980861.

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In this paper, we present a real-time optimal controller, Predictive Steering Control (PSC), to perform high-speed runway exit manoeuvres. PSC is developed based on a time-varying LQR with look-ahead. The aircraft’s ground dynamics are described by a high-fidelity nonlinear model. The proposed controller is compared with an Expert Pilot Model (EPM), which represents a pilot, in several different speed runway exit manoeuvres. With an improved road preview mechanism and optimal feedback gain, the predictive steering controller outperforms the expert pilot’s manual operations by executing the run
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Im, Eunji, Minji Choi, and Kyunghoon Cho. "Model Predictive Control with Variational Autoencoders for Signal Temporal Logic Specifications." Sensors 24, no. 14 (2024): 4567. http://dx.doi.org/10.3390/s24144567.

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This paper presents a control strategy synthesis method for dynamical systems with differential constraints, emphasizing the prioritization of specific rules. Special attention is given to scenarios where not all rules can be simultaneously satisfied to complete a given task, necessitating decisions on the extent to which each rule is satisfied, including which rules must be upheld or disregarded. We propose a learning-based Model Predictive Control (MPC) method designed to address these challenges. Our approach integrates a learning method with a traditional control scheme, enabling the contr
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Xu, Dongxin, Yueqiang Han, Chang Ge, Longtao Qu, Rui Zhang, and Guoye Wang. "A Model Predictive Control Method for Vehicle Drifting Motions with Measurable Errors." World Electric Vehicle Journal 13, no. 3 (2022): 54. http://dx.doi.org/10.3390/wevj13030054.

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Vehicle drifting control has attracted wide attention, and the study methods are divided into expert-based and theory-based. In this paper, the vehicle drifting control was based on the vehicle drifting state characteristics. The vehicle drifting state parameters were obtained by the theory-based vehicle drifting motion mechanism analysis based on a nonlinear vehicle dynamics model, which was used to express the vehicle characteristics, together with the UniTire model, by considering the vehicle longitudinal, lateral, roll, and yaw motions. A vehicle drifting controller was designed by the mod
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Jevtovic, Branislav, and Miroslav Matausek. "A predictive-adaptive hierarchical control system of bucket-wheel excavator: Theory and experimental results." Facta universitatis - series: Electronics and Energetics 18, no. 3 (2005): 493–503. http://dx.doi.org/10.2298/fuee0503493j.

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Development of a new two-level hierarchical control system, which significantly increases excavating capacity, as well as availability, and reliability of the bucket wheel excavator, is presented in this paper. On the first ? basic level functions of local regulators and sensors are executed and the second ? higher level is performing adaptation based on prediction of cutting resistance of materials to be excavated. Development of basic control system consists of design and tuning of local regulators, as well as design of highly precise and reliable sensors of basic movements. The predictive?a
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Cáceres, Gabriela, Pablo Millán, Mario Pereira, and David Lozano. "Smart Farm Irrigation: Model Predictive Control for Economic Optimal Irrigation in Agriculture." Agronomy 11, no. 9 (2021): 1810. http://dx.doi.org/10.3390/agronomy11091810.

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The growth of the global population, together with climate change and water scarcity, has made the shift towards efficient and sustainable agriculture increasingly important. Undoubtedly, the recent development of low-cost IoT-based sensors and actuators offers great opportunities in this direction since these devices can be easily deployed to implement advanced monitoring and irrigation control techniques at a farm scale, saving energy and water and decreasing costs. This paper proposes an economic and periodic predictive controller taking advantage of the irrigation periodicity. The goal of
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Lee, Taekgyu, Dongyoon Seo, Jinyoung Lee, and Yeonsik Kang. "Real-Time Drift-Driving Control for an Autonomous Vehicle: Learning from Nonlinear Model Predictive Control via a Deep Neural Network." Electronics 11, no. 17 (2022): 2651. http://dx.doi.org/10.3390/electronics11172651.

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A drift-driving maneuver is a control technique used by an expert driver to control a vehicle along a sharply curved path or slippery road. This study develops a nonlinear model predictive control (NMPC) method for the autonomous vehicle to perform a drift maneuver and generate the datasets necessary for training the deep neural network(DNN)-based drift controller. In general, the NMPC method is based on numerical optimization which is difficult to run in real-time. By replacing the previously designed NMPC method with the proposed DNN-based controller, we avoid the need for complex numerical
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Safari, Ashkan, Hossein Hassanzadeh Yaghini, Hamed Kharrati, Afshin Rahimi, and Arman Oshnoei. "Voltage Controller Design for Offshore Wind Turbines: A Machine Learning-Based Fractional-Order Model Predictive Method." Fractal and Fractional 8, no. 8 (2024): 463. http://dx.doi.org/10.3390/fractalfract8080463.

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Integrating renewable energy sources (RESs), such as offshore wind turbines (OWTs), into the power grid demands advanced control strategies to enhance efficiency and stability. Consequently, a Deep Fractional-order Wind turbine eXpert control system (DeepFWX) model is developed, representing a hybrid proportional/integral (PI) fractional-order (FO) model predictive random forest alternating current (AC) bus voltage controller designed explicitly for OWTs. DeepFWX aims to address the challenges associated with offshore wind energy systems, focusing on achieving the smooth tracking and state est
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Ugwuanyi, Hyginus Sunday, and Joseph Udokamma Ugwuanyi. "Smart Control Solution for Single-Stage Solar PV Systems." European Journal of Electrical Engineering and Computer Science 7, no. 6 (2023): 38–45. http://dx.doi.org/10.24018/ejece.2023.7.6.582.

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Solar photovoltaic (PV) systems unpredictable characteristics and tight grid-codes demand power electronic-based energy conversion devices. Hence, as the power levels generated by the solar PV systems rise, multi-level voltage source converters (VSC) and their control mechanisms become more necessary for effective energy conversion. Continuous control set model predictive control (CCS-MPC) is a class of predictive control approach that has emerged recently for the applications of power converters and energy conversion systems. In this paper, an artificial neural network (ANN) based controller
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Dissertations / Theses on the topic "Expert model predictive controller"

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Mancino, Francesco. "An embedded model predictive controller for optimal truck driving." Thesis, KTH, Reglerteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-205649.

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An embedded model predictive controller for velocity control of trucks is developed and tested. By using a simple model of a heavy duty vehicle and knowledge about the slope of the road ahead, the fuel consumption while traveling near a set speed is diminished by almost 1% on an example road compared to a rule based speed control system. The problem is formulated as a look-ahead optimization problem were fuel consumption and total trip time have to be minimized. To find the optimal solution dynamic programming is used, and the whole code is designed to run on a Scania gearbox ECU in parallel w
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Juhlin-Henricson, Teddy. "Implementation and Analysis of a Clothoid-based Model Predictive Controller." Thesis, KTH, Skolan för elektro- och systemteknik (EES), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-187688.

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For the last couple of years autonomous driving has increased in popularity as a research area, and it continues to grow. A topic within autonomous driving is path following, which is the subject studied in this project. One of the popular controllers to use for controlling a vehicle is the model predictive controller, because it finds an optimal control input for the vehicle based on the model of the vehicle, and its estimated future behaviour within the prediction horizon - which covers a distance ahead of the vehicle. To increase the length of this distance, one can use a new controller - t
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Xu, Shuqi. "Learning Model Predictive Control for Autonomous Racing : Improvements and Model Variation in Model Based Controller." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-247881.

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In this work, an improved Learning Model Predictive Control (LMPC)architecture for autonomous racing is presented. The controller is referencefree and is able to improve lap time by learning from history data of previouslaps. A terminal cost and a sampled safe set are learned from history data toguarantee recursive feasibility and non-decreasing performance at each lap.Improvements have been proposed to implement LMPC on autonomousracing in a more efficient and reliable way. Improvements have been doneon three aspects. Firstly, system identification has been improved to be runin a more efficie
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Mann, Gustav, and Jakob Luedtke. "Implementation of a Model Predictive Controller in a Spark-Ignition Engine." Thesis, Linköpings universitet, Fordonssystem, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-176534.

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The propulsion of the spark-ignition engine has been investigated and developed during the past century to improve driveability, minimize fuel consumption and emissions, resulting in highly engineered and computerized powertrains. Well balanced engine maps containing coordinated set-points and model-based information sharing have solved the cross-coupling between different control loops. During transitions between the operating conditions a disadvantageous transient behavior that affects the engine performance may occur. By implementing an MPC as a superior controller a nearly optimal control
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Ranjbar, Gigasari Roza. "Model Predictive Controller for large-scale systems - Application to water networks." Electronic Thesis or Diss., Ecole nationale supérieure Mines-Télécom Lille Douai, 2024. http://www.theses.fr/2024MTLD0002.

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Cette thèse aborde le défi de l’optimisation de la gestion des ressources en eau au sein des canaux. Il s’agit d’une tâche particulièrement complexe en raison de leur échelle étendue et de la nature diverse de leurs composants, mais également de leurs dynamiques complexes caractérisées par des retards importants et parfois des pentes nulles. En ce qui concerne les réseaux de voies navigables, l’objectif principal est de mettre en œuvre des techniques issues de la théorie du Contrôle afin d’assurer la navigabilité du réseau, garantissant le respect des niveaux d’eau pour la navigation. Plus pré
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Rogalsky, Dennis Wayne. "Quantifying plant model parameter effects on controller performance /." Thesis, Connect to this title online; UW restricted, 1999. http://hdl.handle.net/1773/9843.

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Campher, Andre Herman. "A systematic approach to model predictive controller constraint handling : rigorous geometric methods." Diss., University of Pretoria, 2011. http://hdl.handle.net/2263/28378.

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The models used by model predictive controllers (MPCs) to predict future outcomes are usually unconstrained forms like impulse or step responses and discrete state space models. Certain MPC algorithms allow constraints to be imposed on the inputs or outputs of a system; but they may be infeasible as they are not checked for consistency via the process model. Consistent constraint handling methods - which account for their interdependence and disambiguate the language used to specify constraints – would therefore be an attractive aid when using any MPC package. A rigorous and systematic approac
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Bangalore, Narendranath Rao Amith Kaushal. "Online Message Delay Prediction for Model Predictive Control over Controller Area Network." Thesis, Virginia Tech, 2017. http://hdl.handle.net/10919/78626.

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Today's Cyber-Physical Systems (CPS) are typically distributed over several computing nodes communicating by way of shared buses such as Controller Area Network (CAN). Their control performance gets degraded due to variable delays (jitters) incurred by messages on the shared CAN bus due to contention and network overhead. This work presents a novel online delay prediction approach that predicts the message delay at runtime based on real-time traffic information on CAN. It leverages the proposed method to improve control quality, by compensating for the message delay using the Model Predictive
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Mattsson, Mathias, and Rasmus Mehler. "Optimal Vehicle Speed Control Using a Model Predictive Controller for an Overactuated Vehicle." Thesis, Linköpings universitet, Fordonssystem, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-119480.

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To control the speed of an overactuated vehicle there may be many possible ways to use the actuators of the car achieving the same outcome. The actuators in an ordinary car is a combustion engine and a friction brake. In some cases it is trivial how to coordinate actuators for the optimal result, but in many cases it is not. The goal with the thesis is to investigate if it is possible to achieve the same or improved performance with a more sophisticated control structure than today's, using a model predictive controller. A model predictive controller combines the possibility to predict the out
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Thorin, Kristoffer. "Optimal Speed Controller in the Presence of Traffic Lights." Thesis, Uppsala universitet, Avdelningen för systemteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-325352.

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This report presents an approach on how to utilize information on future states of traffic lights to reduce the energy consumption and trip time for a Heavy Duty Vehicle. Model Predictive Control is proposed as a solution to handle the optimisation on-line and the concept is tested for various prediction horizons in which information can be received. Further on, it is investigated if the implemented controller is robust enough to execute the same task in a scenario where only the current state is known and future states are predicted. Comparison with a reference vehicle demonstrates improved f
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Books on the topic "Expert model predictive controller"

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An expert system to perform on-line controller restructuring for abrupt model changes. National Aeronautics and Space Administration, 1990.

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Book chapters on the topic "Expert model predictive controller"

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Munoz-Hernandez, German Ardul, Sa’ad Petrous Mansoor, and Dewi Ieuan Jones. "Model Predictive Controller." In Advances in Industrial Control. Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2291-3_11.

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Batur, C., C. C. Chan, and A. Srinivasan. "Fuzzy Model Based Predictive Controller." In Methods and Applications of Intelligent Control. Springer Netherlands, 1997. http://dx.doi.org/10.1007/978-94-011-5498-7_6.

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Singh, Abhaya Pal, Dipankar Deb, Himanshu Agrawal, and Valentina E. Balas. "Fractional Model Predictive and Adaptive Fractional Model Predictive Controller Design." In Fractional Modeling and Controller Design of Robotic Manipulators. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58247-0_4.

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Christofides, Panagiotis D., Jinfeng Liu, and David Muñoz de la Peña. "Distributed Model Predictive Control: Two-Controller Cooperation." In Networked and Distributed Predictive Control. Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-582-8_4.

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Christofides, Panagiotis D., Jinfeng Liu, and David Muñoz de la Peña. "Distributed Model Predictive Control: Multiple-Controller Cooperation." In Networked and Distributed Predictive Control. Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-582-8_5.

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El Hachimi, M., A. Ballouk, and A. Baghdad. "Rapid Model Predictive Controller for Artificial Pancreas." In Recent Advances in Electrical and Information Technologies for Sustainable Development. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05276-8_8.

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Honc, Daniel, and Milan Jičínský. "Analytic Model Predictive Controller in Simple Symbolic Form." In Innovation, Engineering and Entrepreneurship. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91334-6_12.

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Park, On, Hyo-Sang Shin, and Antonios Tsourdos. "Nonlinear Model Predictive Controller-Based Line Tracking Guidance." In Advances in Industrial Control. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-39767-7_2.

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Khalid, Karra, Aziz Derouich, and Mahfoud Said. "Robotic Arm Control Using Dynamic Model Linearization and Model Predictive Controller." In Digital Technologies and Applications. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-29860-8_88.

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Ho, Yvonne. "Model Predictive Controller using Interior Point and Ant Algorithm." In Patient-Specific Controller for an Implantable Artificial Pancreas. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2402-4_5.

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Conference papers on the topic "Expert model predictive controller"

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Mekonen, Eskeziyaw Alemneh, Ermias Kassahun, Kindu Tigabu, Melkamu Bekele, and Admasu Yehule. "Model Predictive Controller Design for Precision Agricultural Robot." In 2024 International Conference on Information and Communication Technology for Development for Africa (ICT4DA). IEEE, 2024. https://doi.org/10.1109/ict4da62874.2024.10777188.

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Wang, Shuai, Yihao Huang, Wang Wei Lee, et al. "A Robust Model Predictive Controller for Tactile Servoing." In 2024 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2024. http://dx.doi.org/10.1109/icra57147.2024.10611317.

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Sun, Liting, Cheng Peng, Wei Zhan, and Masayoshi Tomizuka. "A Fast Integrated Planning and Control Framework for Autonomous Driving via Imitation Learning." In ASME 2018 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/dscc2018-9249.

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Safety and efficiency are two key elements for planning and control in autonomous driving. Theoretically, model-based optimization methods, such as Model Predictive Control (MPC), can provide such optimal driving policies. Their computational complexity, however, grows exponentially with horizon length and number of surrounding vehicles. This makes them impractical for real-time implementation, particularly when nonlinear models are considered. To enable a fast and approximately optimal driving policy, we propose a safe imitation framework, which contains two hierarchical layers. The first lay
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Zhu, K. Y. "Model-based predictive controller." In International Conference on Control '94. IEE, 1994. http://dx.doi.org/10.1049/cp:19940273.

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Batur and Kasparian. "Self-organizing model based expert controller." In IEEE International Conference on Systems Engineering. IEEE, 1989. http://dx.doi.org/10.1109/icsyse.1989.48704.

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Vesely, Vojtech, and Daniel Vozak. "Stable model predictive controller design." In 2014 15th International Carpathian Control Conference (ICCC). IEEE, 2014. http://dx.doi.org/10.1109/carpathiancc.2014.6843687.

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Lima, Pedro F., Goncalo Collares Pereira, Jonas Martensson, and Bo Wahlberg. "Progress Maximization Model Predictive Controller." In 2018 21st International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2018. http://dx.doi.org/10.1109/itsc.2018.8569647.

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Mattar, 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.

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Boshkovski, G., G. Stojanovski, and M. Stankovski. "Development of embedded model predictive controller." In 2017 13th IEEE International Conference on Control & Automation (ICCA). IEEE, 2017. http://dx.doi.org/10.1109/icca.2017.8003038.

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Wenger, Monika, Reinhard Hametner, Alois Zoitl, and Andreas Voigt. "Industrial embedded model predictive controller platform." In Factory Automation (ETFA 2011). IEEE, 2011. http://dx.doi.org/10.1109/etfa.2011.6059212.

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Reports on the topic "Expert model predictive controller"

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Tsidylo, Ivan M., Serhiy O. Semerikov, Tetiana I. Gargula, Hanna V. Solonetska, Yaroslav P. Zamora, and Andrey V. Pikilnyak. Simulation of intellectual system for evaluation of multilevel test tasks on the basis of fuzzy logic. CEUR Workshop Proceedings, 2021. http://dx.doi.org/10.31812/123456789/4370.

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The article describes the stages of modeling an intelligent system for evaluating multilevel test tasks based on fuzzy logic in the MATLAB application package, namely the Fuzzy Logic Toolbox. The analysis of existing approaches to fuzzy assessment of test methods, their advantages and disadvantages is given. The considered methods for assessing students are presented in the general case by two methods: using fuzzy sets and corresponding membership functions; fuzzy estimation method and generalized fuzzy estimation method. In the present work, the Sugeno production model is used as the closest
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de Kemp, E. A., H. A. J. Russell, B. Brodaric, et al. Initiating transformative geoscience practice at the Geological Survey of Canada: Canada in 3D. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/331097.

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Application of 3D technologies to the wide range of Geosciences knowledge domains is well underway. These have been operationalized in workflows of the hydrocarbon sector for a half-century, and now in mining for over two decades. In Geosciences, algorithms, structured workflows and data integration strategies can support compelling Earth models, however challenges remain to meet the standards of geological plausibility required for most geoscientific studies. There is also missing links in the institutional information infrastructure supporting operational multi-scale 3D data and model develo
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de Kemp, E. A., H. A. J. Russell, B. Brodaric, et al. Initiating transformative geoscience practice at the Geological Survey of Canada: Canada in 3D. Natural Resources Canada/CMSS/Information Management, 2023. http://dx.doi.org/10.4095/331871.

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Application of 3D technologies to the wide range of Geosciences knowledge domains is well underway. These have been operationalized in workflows of the hydrocarbon sector for a half-century, and now in mining for over two decades. In Geosciences, algorithms, structured workflows and data integration strategies can support compelling Earth models, however challenges remain to meet the standards of geological plausibility required for most geoscientific studies. There is also missing links in the institutional information infrastructure supporting operational multi-scale 3D data and model develo
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DCE-MRI V.2, Consensus QIBA Profile. Chair Hendrik Laue and James O'Connor. Radiological Society of North America (RSNA)/Quantitative Imaging Biomarkers Alliance (QIBA), 2023. https://doi.org/10.1148/qiba/20231206.

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The goal of the DCE-MRI quantification QIBA Profile version 2.0 is to provide an update from the Dynamic Contrast Enhanced MRI (DCE-MRI) Quantification profile (version 1.0, dated July 1, 2012) in order to include the use of 3 Tesla (T) MRI and the use of parallel imaging with receiver coil arrays. While many pharmacokinetic models have been described, this QIBA Profile (DCE-MRI Quantification) specifically addresses the physiological parameter Ktrans derived from the Tofts or generalized kinetic model (GKM) (1), which is correlated with the vessel (surface/area product and permeability) and h
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