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Citácie

  • Celkový počet citácií       253

K. KišM. Klaučo: Neural network based explicit MPC for chemical reactor control. Acta Chimica Slovaca, č. 2, zv. 12, str. 218–223, 2019.
  • Počet citácií       1
  • Shin, Yeonju – Smith, Robin – Hwang, Sungwon: Development of model predictive control system using an artificial neural network: A case study with a distillation column. Journal of Cleaner Production, č. 124124, zv. 277, 2020.
M. KalúzM. KlaučoĽ. ČirkaM. Fikar: Flexy2: A Portable Laboratory Device for Control Engineering Education. V 12th IFAC Symposium Advances in Control Education, str. 159–164, 2019.
  • Počet citácií       4
  • Marin, Loreto – Vargas, Hector – Heradio, Ruben – de la Torre, Luis – Diaz, Jose Manuel – Dormido, Sebastian: Evidence-Based Control Engineering Education: Evaluating the LCSD Simulation Tool. IEEE Access, zv. 8, str. 170183-170194, 2020.
  • J. L. Villa – S. Sanchez: Implementing a Software-based Controller as a Strategy for Teaching Digital Control. V 2020 IX International Congress of Mechatronics Engineering and Automation (CIIMA), str. 1-6, 2020.
  • Opris, Ioana – Gogoase Nistoran, Daniela E. – Costinas, Sorina – Ionescu, Cristina S.: Rethinking power engineering education for Generation Z. Computer Applications in Engineering Education, č. 1, SI, zv. 29, str. 287-305, 2021.
  • Dusek, F. – Honc, D. – Mrazek, M.: RCDue - Laboratory System for Teaching Automation and Control - Concept of the system. V Proceedings of the 2021 23rd International Conference on Process Control, PC 2021, str. 249-254, 2021.
Y. Lohr – M. KlaučoM. Kalúz – M. Mönnigmann: Mimicking Predictive Control with Neural Networks in Domestic Heating Systems. Editor(i): M. Fikar and M. Kvasnica, V Proceedings of the 22nd International Conference on Process Control, Slovak Chemical Library, Štrbské Pleso, Slovakia, str. 19–24, 2019.
  • Počet citácií       1
  • M. Furka – K. Kiš – M. Horváthová – M. Mojto – M. Bakošová: Identification and Control of a Cascade of Biochemical Reactors. V 2020 Cybernetics Informatics (K I), str. 1-6, 2020.
M. KvasnicaP. BakaráčM. Klaučo: Complexity reduction in explicit MPC: A reachability approach. Systems & Control Letters, zv. 124, str. 19–26, 2019.
  • Počet citácií       3
  • Mönnigmann, M. – Pannocchia, G.: Reducing the computational effort of MPC with closed-loop optimal sequences of affine laws. V IFAC-PapersOnLine, str. 11344-11349, 2020.
  • Maddalena, E.T. – da Moraes, C.G.S. – Waltrich, G. – Jones, C.N.: A neural network architecture to learn explicit MPC controllers from data. V IFAC-PapersOnLine, str. 11362-11367, 2020.
  • Bélai, I. – Huba, M. – Vrancic, D.: Comparing traditional and constrained disturbance-observer based positional control. Measurement and Control (United Kingdom), č. 3-4, zv. 54, str. 170-178, 2021.
M. KlaučoM. KalúzM. Kvasnica: Machine learning-based warm starting of active set methods in embedded model predictive control. Engineering Applications of Artificial Intelligence, zv. 77, str. 1–8, 2019.
  • Počet citácií       23
  • Masti, Daniele – Bemporad, Alberto: Learning binary warm starts for multiparametric mixed-integer quadratic programming. V 2019 18th European Control Conference (ECC), str. 1494-1499, 2019.
  • Nouwens, S.A.N. – de Jager, B. – Paulides, M. – Heemels, W.P.M.H.: Constraint-adaptive MPC for large-scale systems: Satisfying state constraints without imposing them. V IFAC-PapersOnLine, str. 232-237, 2021.
  • Schwenkel, Lukas – Gharbi, Meriem – Trimpe, Sebastian – Ebenbauer, Christian: Online learning with stability guarantees: A memory-based warm starting for real-time MPC. Automatica, č. 109247, zv. 122, 2020.
  • Sabir, Zulqurnain – Raja, Muhammad Asif Zahoor – Guirao, Juan L. G. – Shoaib, Muhammad: Integrated intelligent computing with neuro-swarming solver for multi-singular fourth-order nonlinear Emden-Fowler equation. Computational & Applied Mathematics, č. 4, zv. 39, 2020.
  • Vaupel, Yannic – Hamacher, Nils C. – Caspari, Adrian – Mhamdi, Adel – Kevrekidis, Ioannis G. – Mitsos, Alexander: Accelerating nonlinear model predictive control through machine learning. Journal of Process Control, č. NM0HO, zv. 92, str. 261-270, 2020.
  • Bertsimas, Dimitris – Stellato, Bartolomeo: The voice of optimization. Machine Learning, č. MM4AL, 2020.
  • Umar, Muhammad – Sabir, Zulqurnain – Amin, Fazli – Guirao, Juan L. G. – Raja, Muhammad Asif Zahoor: Stochastic numerical technique for solving HIV infection model of CD4(+) T cells. European Physical Journal Plus, č. 6, zv. 135, 2020.
  • Leal, Adonis F. R. – Rakov, V. A. – Alves, Elton Rafael – Lopes, Marcio N. G.: Estimation of -CG lightning distances using single-station E-field measurements and machine learning techniques. V 2019 International Symposium on Lightning Protection (xv Sipda), 2019.
  • Ihtesham Jadoon – Ashfaq Ahmed – Ata ur Rehman – Muhammad Shoaib – Muhammad Asif Zahoor Raja: Integrated meta-heuristics finite difference method for the dynamics of nonlinear unipolar electrohydrodynamic pump flow model. Applied Soft Computing, zv. 97, str. 106791, 2020.
  • Umar, M. – Sabir, Z. – Raja, M.A.Z. – Sánchez, Y.G.: A stochastic numerical computing heuristic of SIR nonlinear model based on dengue fever. Results in Physics, č. 103585, zv. 19, 2020.
  • Li, Z. – Xu, H.: Analysis of Working Characteristics of Buck Converter in Artificial Intelligence Background. Advances in Intelligent Systems and Computing (Conference Paper), zv. 1088, str. 529-537, 2020.
  • Sabir, Z. – Nisar, K. – Zahoor Raja, M.A. – Haque, M.R. – Umar, M. – Ag Ibrahim, A.A. – Le, D.-N.: IoT Technology Enabled Heuristic Model with Morlet Wavelet Neural Network for Numerical Treatment of Heterogeneous Mosquito Release Ecosystem. IEEE Access, zv. 9, str. 132897-132913, 2021.
  • Bertsimas, D. – Stellato, B.: The voice of optimization. Machine Learning, č. 2, zv. 110, str. 249-277, 2021.
  • Sabir, Z. – Khalique, C.M. – Raja, M.A.Z. – Baleanu, D.: Evolutionary computing for nonlinear singular boundary value problems using neural network, genetic algorithm and active-set algorithm. European Physical Journal Plus, č. 2, zv. 136, 2021.
  • Stomberg, G. – Engelmann, A. – Faulwasser, T.: A distributed active set method for model predictive control. V IFAC-PapersOnLine, str. 263-268, 2021.
  • Liu, W. – Zheng, Y. – Chen, Q. – Geng, D.: An adaptive CGPC based anti-windup PI controller with stability constraints for the intermittent power penetrated system. International Journal of Electrical Power and Energy Systems, č. 106922, zv. 130, 2021.
  • Sabir, Z. – Ag Ibrahim, A.A. – Raja, M.A.Z. – Nisar, K. – Umar, M. – Rodrigues, J.J.P.C. – Mahmoud, S.R.: Soft computing paradigms to find the numerical solutions of a nonlinear influenza disease model. Applied Sciences (Switzerland), č. 18, zv. 11, 2021.
  • Hu, W. – Zhou, Y. – Zhang, Z. – Fujita, H.: Model Predictive Control for Hybrid Levitation Systems of Maglev Trains with State Constraints. IEEE Transactions on Vehicular Technology, č. 10, zv. 70, str. 9972-9985, 2021.
  • Norouzi, A. – Heidarifar, H. – Shahbakhti, M. – Koch, C.R. – Borhan, H.: Model predictive control of internal combustion engines: A review and future directions. Energies, č. 19, zv. 14, 2021.
  • Sabir, Z. – Raja, M.A.Z. – Baleanu, D. – Cengiz, K. – Shoaib, M.: Design of Gudermannian Neuroswarming to solve the singular Emden–Fowler nonlinear model numerically. Nonlinear Dynamics, č. 4, zv. 106, str. 3199-3214, 2021.
  • Ławryńczuk, M.: Introduction to Model Predictive Control. Studies in Systems, Decision and Control, zv. 389, str. 3-40, 2022.
  • Chen, S.W. – Wang, T. – Atanasov, N. – Kumar, V. – Morari, M.: Large scale model predictive control with neural networks and primal active sets. Automatica, č. 109947, zv. 135, 2022.
  • Sabir, Z. – Raja, M.A.Z. – Botmart, T. – Weera, W.: A Neuro-Evolution Heuristic Using Active-Set Techniques to Solve a Novel Nonlinear Singular Prediction Differential Model. Fractal and Fractional, č. 1, zv. 6, 2022.
P. BakaráčJ. HolazaM. KlaučoM. Kalúz – J. Löfberg – M. Kvasnica: Explicit MPC based on Approximate Dynamic Programming. V European Control Conference 2018, Limassol, Cyprus, str. 1172–1177, 2018.
  • Počet citácií       3
  • Moennigmann, Martin: On the structure of the set of active sets in constrained linear quadratic regulation. Automatica, zv. 106, str. 61-69, 2019.
  • Gulan, M. – Minarcik, P. – Kulhanek, J.: Energy-efficient Swing-up and MPC Stabilization of an Inverted Pendulum. V Proceedings of the 2019 22nd International Conference on Process Control, PC 2019, str. 209-214, 2019.
  • Boumaza, H. – Belarbi, K.: Optimal model predictive control solution approximation using Takagi Sugeno for linear and a class of nonlinear systems. International Journal of Dynamics and Control, 2021.
P. BakaráčM. KlaučoM. Fikar: Comparison of Inverted Pendulum Stabilization with PID, LQ, and MPC Control. Editor(i): J. Cigánek, Š. Kozák, A. Kozáková, V 2018 Cybernetics & Informatics (K&I), Slovak Chemical Library, Bratislava, Lazy pod Makytou, Slovakia, zv. 29, 2018.
  • Počet citácií       7
  • A. Barkat – A. Hanif – M. T. Hamayun: Model Identification and Control of a Lab Based Inverted Pendulum System Using Robust Control Technique. V 2018 International Conference on Frontiers of Information Technology (FIT), str. 1-6, 2018.
  • Manai, N.E. – Saidi, I. – Soudani, D.: Predictive control of an under-actuated System. V Proceedings of International Conference on Advanced Systems and Emergent Technologies, IC_ASET 2019, str. 90-95, 2019.
  • Jayaprakash, A.K. – Kidambi, K.B. – Mackunis, W. – Drakunov, S.V. – Reyhanoglu, M.: Finite-time state estimation for an inverted pendulum under input-multiplicative uncertainty. Robotics, č. 4, zv. 9, str. 1-26, 2020.
  • Hidayati, A.N. – Wasiwitono, U.: Modeling and Control of Inertia Wheel Pendulum System with LQR and PID control. V Proceedings - 2021 International Seminar on Intelligent Technology and Its Application: Intelligent Systems for the New Normal Era, ISITIA 2021, str. 135-140, 2021.
  • Ontiveros-Robles, E. – Melin, P. – Castillo, O.: An Efficient High-Order α-Plane Aggregation in General Type-2 Fuzzy Systems Using Newton–Cotes Rules. International Journal of Fuzzy Systems, č. 4, zv. 23, str. 1102-1121, 2021.
  • Umamaheswari, K. – Prabhakar, G. – Viji, K. – Thanapal, P.: ANFIS PD Plus I Control On Simscape Model of Nonlinear Physical System. Control Engineering and Applied Informatics, č. 1, zv. 23, str. 50-59, 2021.
  • Mousa, M.E. – Ebrahim, M.A. – Zaky, M.M. – Saied, E.M. – Kotb, S.A.: Hybrid optimization technique for enhancing the stability of inverted pendulum system. International Journal of Swarm Intelligence Research, č. 1, zv. 12, str. 1-16, 2021.
J. HolazaM. Klaučo – J. Drgoňa – J. OravecM. KvasnicaM. Fikar: MPC-Based Reference Governor Control of a Continuous Stirred-Tank Reactor. Computers & Chemical Engineering, zv. 165, str. 289–299, 2018.
  • Počet citácií       16
  • Lorena Garzon-Castro, Claudia – Delgado-Aguilera, Efredy – Alexander Cortes-Romero, John – Tello, Edisson – Mazzanti, Gianfranco: Performance of an active disturbance rejection control on a simulated continuous microalgae photobioreactor. Computers & Chemical Engineering, zv. 117, str. 129-144, 2018.
  • Garzón-Castro, C.L. – Delgado-Aguilera, E. – Cortés-Romero, J.A. – Tello, E. – Mazzanti, G.: Performance of an active disturbance rejection control on a simulated continuous microalgae photobioreactor. Computers and Chemical Engineering, zv. 117, str. 129-144, 2018.
  • Aliskan, I.: Adaptive Model Predictive Control for Wiener Nonlinear Systems. Iranian Journal of Science and Technology - Transactions of Electrical Engineering, zv. 43, str. 361-377, 2019.
  • Edwin, E.L.R. – Garcia, C.: Predictive controller applied to a pH neutralization process. V IFAC-PapersOnLine, str. 202-206, 2019.
  • Aliskan, Ibrahim: A Novel Fuzzy PI Control Approach for Nonlinear Processes. Arabian Journal for Science and Engineering, č. 8, zv. 45, str. 6821-6834, 2020.
  • Xu, Hansong – Liu, Xing – Yu, Wei – Griffith, David – Golmie, Nada: Reinforcement Learning-Based Control and Networking Co-Design for Industrial Internet of Things. IEEE Journal on Selected Areas in Communications, č. 5, zv. 38, str. 885-898, 2020.
  • Garzon-Castro, C.L. – Cardona, M. – Velazquez, R. – Del-Valle-Soto, C.: Intelligent PI controller for microalgae growth in a closed photobioreactor. V 2020 IEEE ANDESCON, ANDESCON 2020, 2020.
  • Balula, S. – Liniger, A. – Rupenyan, A. – Lygeros, J.: Reference design for closed loop system optimization. V European Control Conference 2020, ECC 2020, str. 650-655, 2020.
  • Xu, H. – Liu, X. – Yu, W. – Griffith, D. – Golmie, N.: Reinforcement Learning-Based Control and Networking Co-Design for Industrial Internet of Things. IEEE Journal on Selected Areas in Communications, č. 5, zv. 38, str. 885-898, 2020.
  • Ortiz, O.J.R. – Castelblanco, J.S.U. – Fonseca, G.L.V.: MRAC and MPC Controllers for Load Application System of the Accelerated Testing Equipment of Pavements. International Journal on Advanced Science, Engineering and Information Technology, č. 5, zv. 10, str. 1946-1953, 2020.
  • Adam, E.J. – Pipino, H.A. – Cappelletti, C.A.: Adaptive multi-model predictive control applied to continuous stirred tank reactor. Computers and Chemical Engineering, č. 107195, zv. 145, 2021.
  • Farajzadeh-D, Mohammad-G – Sani, S. K. Hosseini: An improved two-loop model predictive control design for nonlinear robust reference tracking with practical advantages. Optimal Control Applications & Methods, č. 2, zv. 42, str. 548-565, 2021.
  • Paulusova, Jana – Vesely, Vojtech: Optimal Offline MPC Design: Output Feedback. International Journal of Innovative Computing Information and Control, č. 2, zv. 17, str. 461-472, 2021.
  • Sangregorio-Soto, Viyils – Garzon-Castro, Claudia L. – Mazzanti, Gianfranco – Figueredo, Manuel – Cortes-Romero, John A.: Proportional-Integral Controller Assisted by GPI Observer for Microalgal Continuous Culture. V 2020 Argentine Conference on Automatic Control (aadeca), 2020.
  • Aliskan, Ibrahim: Optimized Inverse Nonlinear Function-Based Wiener Model Predictive Control for Nonlinear Systems. Arabian Journal for Science and Engineering, č. 10, zv. 46, str. 10217-10230, 2021.
  • He, H. – Chen, Y. – Qi, W. – Wang, M. – Chen, X.: Observer-based resilient control of positive systems with heterogeneous DoS attacks: A Markov model approach. Journal of the Franklin Institute, 2021.
M. KlaučoR. Valo – J. Drgoňa: Reflux control of a laboratory distillation column via MPC-based reference governor. Acta Chimica Slovaca, č. 2, zv. 10, str. 139–143, 2017.
  • Počet citácií       1
  • Bakarac, Peter – Kvasnica, Michal: Approximate explicit robust model predictive control of a CSTR with fast reactions. Chemical Papers, č. 3, zv. 73, str. 611-618, 2019.
J. HolazaM. KlaučoM. Kvasnica: Solution Techniques for Multi-Layer MPC-Based Control Strategies. V Preprints of the 20th IFAC World Congress, Toulouse, France, zv. 20, 2017.
  • Počet citácií       2
  • de Almeida, Fabio A.: Constrained dynamic compensation with model predictive control for tracking. Aerospace Science and Technology, č. UNSP 105340, zv. 93, 2019.
  • Shao, T.: Indoor Environment Intelligent Control System of Green Building Based on PMV Index. Advances in Civil Engineering, č. 6619401, zv. 2021, 2021.
F. Janeček – M. KlaučoM. KalúzM. Kvasnica: OPTIPLAN: A Matlab Toolbox for Model Predictive Control with Obstacle Avoidance. V Preprints of the 20th IFAC World Congress, Toulouse, France, zv. 20, 2017.
  • Počet citácií       9
  • Ioan, Daniel – Olaru, Sorin – Prodan, Ionela – Stoican, Florin – Niculescu, Silviu-Iulian: Navigation in a multi-obstacle environment. From partition of the space to a zonotopic-based MPC. V 2019 18th European Control Conference (ECC), str. 1772-1777, 2019.
  • Stoican, Florin – Prodan, Ionela – Grotli, Esten Ingar: Exact and overapproximated guarantees for corner cutting avoidance in a multiobstacle environment. International Journal of Robust and Nonlinear Control, č. 15, zv. 28, str. 4528-4548, 2018.
  • Ioan, D. – Prodan, I. – Stoican, F. – Olaru, S. – Niculescu, S.-I.: Complexity bounds for obstacle avoidance within a zonotopic framework. V Proceedings of the American Control Conference, str. 335-340, 2019.
  • Ioan, D. – Olaru, S. – Prodan, I. – Stoican, F. – Niculescu, S.-I.: Parametrized Hyperplane Arrangements for Control Design with Collision Avoidance Constraints. V IEEE International Conference on Control and Automation, ICCA, str. 1591-1596, 2019.
  • Ioan, D. – Prodan, I. – Olaru, S. – Stoican, F. – Niculescu, S.-I.: Mixed-integer programming in motion planning. Annual Reviews in Control, 2020.
  • Ioan, D. – Olaru, S. – Prodan, I. – Stoican, F. – Niculescu, S.-I.: From Obstacle-Based Space Partitioning to Corridors and Path Planning. A Convex Lifting Approach. IEEE Control Systems Letters, č. 1, zv. 4, str. 79-84, 2020.
  • Reiter, R. – Kirchengast, M. – Watzenig, D. – Diehl, M.: Mixed-integer optimization-based planning for autonomous racing with obstacles and rewards. V IFAC-PapersOnLine, str. 99-106, 2021.
  • Kochdumper, N. – Gruber, F. – Schürmann, B. – Gaßmann, V. – Klischat, M. – Althoff, M.: AROC: A toolbox for automated reachset optimal controller synthesis. V HSCC 2021 - Proceedings of the 24th International Conference on Hybrid Systems: Computation and Control (part of CPS-IoT Week), 2021.
  • Ioan, D. – Prodan, I. – Olaru, S. – Stoican, F. – Niculescu, S.-I.: Mixed-integer programming in motion planning. Annual Reviews in Control, zv. 51, str. 65-87, 2021.
M. Klaučo: MPC-based Reference Governors: Theory and Applications. ÚIAM FCHPT STU v Bratislave, Radlinského 9, 812 37 Bratislava, 2017.
  • Počet citácií       1
  • Siddiqui, I. – Ingole, D. – Sonawane, D. – Agashe, S.: Offset-free nonlinear model predictive control of a drum-boiler pilot plant. V IFAC-PapersOnLine, str. 506-511, 2020.
J. HolazaR. ValoM. Klaučo: A Novel Approach of Control Design of the pH in the Neutralization Reactor. Editor(i): M. Fikar and M. Kvasnica, V Proceedings of the 21st International Conference on Process Control, Slovak Chemical Library, Štrbské Pleso, Slovakia, str. 191–196, 2017.
  • Počet citácií       1
  • Rose, T.P. – Devadhas, G.G.: Detection of pH neutralization technique in multiple tanks using ANFIS controller. Microprocessors and Microsystems, č. 102845, zv. 72, 2020.
F. Janeček – M. KlaučoM. Kvasnica: Trajectory Planning and Following for UAVs with Nonlinear Dynamics. Editor(i): M. Fikar and M. Kvasnica, V Proceedings of the 21st International Conference on Process Control, Slovak Chemical Library, Štrbské Pleso, Slovakia, str. 333–338, 2017.
  • Počet citácií       2
  • Ioan, D. – Prodan, I. – Olaru, S. – Stoican, F. – Niculescu, S.-I.: Mixed-integer programming in motion planning. Annual Reviews in Control, 2020.
  • Ioan, D. – Prodan, I. – Olaru, S. – Stoican, F. – Niculescu, S.-I.: Mixed-integer programming in motion planning. Annual Reviews in Control, zv. 51, str. 65-87, 2021.
J. OravecM. KlaučoM. Kvasnica – J. Löfberg: Computationally Tractable Formulations for Optimal Path Planning with Interception of Targets’ Neighborhoods. Journal of Guidance, Control, and Dynamics, č. 5, zv. 40, str. 1221–1230, 2017.
  • Počet citácií       1
  • Q. Hu – J. Xie – X. Liu: Trajectory optimization for accompanying satellite obstacle avoidance. Aerospace Science and Technology, 2018.
  • Počet citácií       44
  • Findeisen, R. – Graichen, K. – Mönnigmann, M.: Embedded optimization in control: An introduction, opportunities, and challenges [Eingebettete Optimierung in der Regelungstechnik - Grundlagen und Herausforderungen]. At-Automatisierungstechnik, č. 11, zv. 66, str. 877-902, 2018.
  • Swetha, C – Mohan, Dhanoj – Devadhas, G Glan – Augustine, Clint: Control Analysis of Magnetic Levitation System. V 2018 International Conference on Control, Power, Communication and Computing Technologies (ICCPCCT), str. 600--604, 2018.
  • Cavanini, Luca – Cimini, Gionata – Ippoliti, Gianluca: Model predictive control for pre-compensated power converters: Application to current mode control. Journal of the Franklin Institute-engineering and Applied Mathematics, č. 4, zv. 356, str. 2015-2030, 2019.
  • Farajzadeh-Devin, Mohammad-Ghassem – Hosseini Sani, Seyed Kamal: Enhanced two-loop model predictive control design for linear uncertain systems. Journal of Systems Engineering and Electronics, č. 1, zv. 32, str. 220-227, 2021.
  • La Delfa, S. – Enjalbert, S. – Polet, P. – Vanderhaegen, F.: Design of a cooperative eco-driving rail control system: an experimental study. Cognition, Technology and Work, 2019.
  • Farajzadeh-D, Mohammad-G – Sani, S. K. Hosseini: An improved two-loop model predictive control design for nonlinear robust reference tracking with practical advantages. Optimal Control Applications & Methods, č. OM2UV, 2020.
  • Ogumerem, Gerald S. – Pistikopoulos, Efstratios N.: Parametric optimization and control for a smart Proton Exchange Membrane Water Electrolysis (PEMWE) system. Journal of Process Control, č. MC5ZG, zv. 91, str. 37-49, 2020.
  • Zhang, Zhenlin – Zhou, Yonghua – Tao, Xin: Model predictive control of a magnetic levitation system using two-level state feedback. Measurement & Control, č. 5-6, zv. 53, str. 962-970, 2020.
  • Raha, Arnab – Chakrabarty, Ankush – Raghunathan, Vijay – Buzzard, Gregery T.: Embedding Approximate Nonlinear Model Predictive Control at Ultrahigh Speed and Extremely Low Power. IEEE Transactions on Control Systems Technology, č. 3, zv. 28, str. 1092-1099, 2020.
  • Fatemimoghadam, Armita – Toshani, Hamid – Manthouri, Mohammad: Control of magnetic levitation system using recurrent neural network-based adaptive optimal backstepping strategy. Transactions of the Institute of Measurement and Control, č. 13, zv. 42, str. 2382-2395, 2020.
  • Mughees, Abdullah – Mohsin, Syed Ali: Design and Control of Magnetic Levitation System by Optimizing Fractional Order PID Controller Using Ant Colony Optimization Algorithm. IEEE Access, č. ML0MY, zv. 8, str. 116704-116723, 2020.
  • L. Dutta – D. Kumar Das: A Linear Model Predictive Control design for Magnetic Levitation System. V 2020 International Conference on Computational Performance Evaluation (ComPE), str. 039-043, 2020.
  • M. Hypiusová – D. Rosinová – A. Kozáková: Comparison of State Feedback Controllers for the Magnetic Levitation System. V 2020 Cybernetics Informatics (K I), str. 1-6, 2020.
  • Hu, Wenjie – Zhou, Yonghua – Zhang, Zhenlin – Fujita, Hamido: Model Predictive Control for Hybrid Levitation Systems of Maglev Trains With State Constraints. IEEE Transactions on Vehicular Technology, č. 10, zv. 70, str. 9972-9985, 2021.
  • Raha, Arnab – Chakrabarty, Ankush – Raghunathan, Vijay – Buzzard, Gregery T.: Embedding Approximate Nonlinear Model Predictive Control at Ultrahigh Speed and Extremely Low Power. IEEE Transactions on Control Systems Technology, č. 3, zv. 28, str. 1092-1099, 2020.
  • Rosinova, Danica – Hypiusova, Maria: Control Education on Magnetic Levitation System. V Process Control `21 - Proceeding of the 2021 23rd International Conference on Process Control (pc), str. 131-136, 2021.
  • Zavadsky, V. K. – Ivanov, V. P. – Kablova, E. B. – Clenovaya, L. G.: Quasi-Terminal Controllers Synthesis. Automation and Remote Control, č. 3, zv. 82, str. 526-536, 2021.
  • Molina, Luis M. Castellanos – Bonfitto, Angelo – Galluzzi, Renato: Offset-Free Model Predictive Control for a cone-shaped active magnetic\\n bearing system. Mechatronics, č. 102612, zv. 78, 2021.
  • Hu, Wenjie – Zhou, Yonghua – Zhang, Zhenlin – Fujita, Hamido: Model Predictive Control for Hybrid Levitation Systems of Maglev Trains\\n With State Constraints. IEEE Transactions on Vehicular Technology, č. 10, zv. 70, str. 9972-9985, 2021.
  • Vrlic, Martin – Ritzberger, Daniel – Jakubek, Stefan: Model-Predictive-Control-Based Reference Governor for Fuel Cells in Automotive Application Compared with Performance from a Real Vehicle. Energies, č. 8, zv. 14, 2021.
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