Ústav informatizácie, automatizácie a matematiky

doc. Ing. MSc. Martin Klaučo, PhD.

Pracovné zaradenie:
Vedúci oddelenia
Vedecko-výskumný pracovník
Oddelenie:
Oddelenie informatizácie a riadenia procesov (OIaRP)
Miestnosť:
NB 663
eMail:
Webová stránka:
https://www.uiam.sk/~klauco
Telefón:
+421 259 325 345
Skype:
m.klauco
ORCID iD:
0000-0003-0098-2625
WoS ResearcherID:
G-3973-2015
Google Scholar:
wVXDzr8AAAAJ
Dostupnosť:

Citácie

  • Celkový počet citácií       262
K. Kiš – M. 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. Kalúz – M. Klaučo – Ľ. Čirka – M. Fikar: Flexy2: A Portable Laboratory Device for Control Engineering Education. V 12th IFAC Symposium Advances in Control Education, str. 159–164, 2019.
  • Počet citácií       7
  • 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.
  • Sotelo, David – Sotelo, Carlos – Ramirez-Mendoza, Ricardo A. – Lopez-Guajardo, Enrique A. – Navarro-Duran, David – Nino-Juarez, Elvira – Vargas-Martinez, Adriana: Lab-Tec@Home: A Cost-Effective Kit for Online Control Engineering Education. Electronics, č. 6, zv. 11, 2022.
  • Oliveira, P. B. de Moura – Soares, Filomena – Cardoso, Alberto: Pocket-Sized Portable Labs: Control Engineering Practice Made Easy in Covid-19 Pandemic Times. Ifac Papersonline, č. 17, zv. 55, str. 150-155, 2022.
  • Pajpach, Martin – Haffner, Oto – Kucera, Erik – Drahos, Peter: Low-Cost Education Kit for Teaching Basic Skills for Industry 4.0 Using Deep-Learning in Quality Control Tasks. Electronics, č. 2, zv. 11, 2022.
Y. Lohr – M. Klaučo – M. 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. Kvasnica – P. Bakaráč – M. 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. Klaučo – M. Kalúz – M. 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. Bakaráč – J. Holaza – M. Klaučo – M. 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. Bakaráč – M. Klaučo – M. 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       8
  • 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.
  • Pham, D.-B. – Pham, D.-T. – Dao, Q.-T. – Nguyen, V.-A.: Takagi-Sugeno Fuzzy Control for Stabilizing Nonlinear Inverted Pendulum. Lecture Notes in Networks and Systems, zv. 471 LNNS, str. 333-341, 2022.
J. Holaza – M. Klaučo – J. Drgoňa – J. Oravec – M. Kvasnica – M. Fikar: MPC-Based Reference Governor Control of a Continuous Stirred-Tank Reactor. Computers & Chemical Engineering, zv. 165, str. 289–299, 2018.
  • Počet citácií       19
  • 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.
  • Edwin, E.L.R. – Garcia, C.: Predictive controller applied to a pH neutralization process. V IFAC-PapersOnLine, str. 202-206, 2019.
  • Pipino, Hugo A. – Cappelletti, Carlos A. – Adam, Eduardo J.: Adaptive multi-model predictive control applied to continuous stirred tank reactor. Computers & Chemical Engineering, č. 107195, zv. 145, 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.
  • 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.
  • 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.
  • 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.
  • Balula, Samuel – Liniger, Alex – Rupenyan, Alisa – Lygeros, John: Reference design for closed loop system optimization. V 2020 European Control Conference (ECC 2020), str. 650-655, 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, Hangfeng – Chen, Yi – Qi, Wenhai – Wang, Maoli – Chen, Xiaoming: Observer-based resilient control of positive systems with heterogeneous DoS attacks: A Markov model approach. Journal of the Franklin Institute-engineering and Applied Mathematics, č. 1, zv. 359, str. 272-293, 2022.
  • Pappenreiter, Magdalena – Doebele, Sebastian – Striedner, Gerald – Jungbauer, Alois – Sissolak, Bernhard: Model predictive control for steady-state performance in integrated continuous bioprocesses. Bioprocess and Biosystems Engineering, č. 9, zv. 45, str. 1499-1513, 2022.
  • 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.
  • Wu, Hui – Yan, Fei – Wang, Guangjun – Lv, Cai: A predictive control based on decentralized fuzzy inference for a pH neutralization process. Journal of Process Control, zv. 110, str. 76-83, 2022.
  • 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.
  • Aliskan, Ibrahim: Adaptive Model Predictive Control for Wiener Nonlinear Systems. Iranian Journal of Science and Technology-transactions of Electrical Engineering, č. 1, zv. 43, str. 361-377, 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.
  • Muhammed, Alhelou – Yazan, Dayoub – Gavrilov, Alexander: Reference governed ADRC approach to manage the handling-comfort contradiction in a full-vehicle suspension. Transactions of the Institute of Measurement and Control, č. 14, zv. 44, str. 2693-2708, 2022.
M. Klaučo – R. 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. Holaza – M. Klaučo – M. 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. Klaučo – M. Kalúz – M. 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. Holaza – R. Valo – M. 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. Klaučo – M. 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. Oravec – M. Klaučo – M. 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.
M. Klaučo – M. Kalúz – M. Kvasnica: Real-time implementation of an explicit MPC-based reference governor for control of a magnetic levitation system. Control Engineering Practice, č. 60, str. 99–105, 2017.
  • 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.
  • Zhang, Kaiyang – Xu, Fengqiu – Xu, Xianze: Observer-based fast nonlinear MPC for multi-DOF maglev positioning system: Theory and experiment. Control Engineering Practice, č. 104860, zv. 114, 2021.
  • Molina, Luis M. Castellanos – Bonfitto, Angelo – Galluzzi, Renato: Offset-Free Model Predictive Control for a cone-shaped active magnetic bearing system. Mechatronics, č. 102612, zv. 78, 2021.
  • Tang, Jiawei – Huang, Zhiwen – Zhu, Yidan – Zhu, Jianmin: Neural network compensation control of magnetic levitation ball position based on fuzzy inference. Scientific Reports, č. 1, zv. 12, 2022.
  • Zhang, Zhenlin – Zhou, Yonghua – Tao, Xin: Model predictive control of a magnetic levitation system using two-level\\n state feedback. Measurement & Control, č. 5-6, zv. 53, str. 962-970, 2020.
  • Farajzadeh-Devin, Mohammad-Ghassem – Hosseini Sani, Seyed Kamal: Enhanced two-loop model predictive control design for linear uncertain\\n systems. Journal of Systems Engineering and Electronics, č. 1, zv. 32, str. 220-227, 2021.
  • Farajzadeh-D, Mohammad-G – Sani, S. K. Hosseini: An improved two-loop model predictive control design for nonlinear\\n robust reference tracking with practical advantages. Optimal Control Applications & Methods, č. 2, zv. 42, str. 548-565, 2021.
  • Ogumerem, Gerald S. – Pistikopoulos, Efstratios N.: Parametric optimization and control for a smart Proton Exchange Membrane\\n Water Electrolysis (PEMWE) system. Journal of Process Control, zv. 91, str. 37-49, 2020.
  • Vrlic, Martin – Ritzberger, Daniel – Jakubek, Stefan: Model-Predictive-Control-Based Reference Governor for Fuel Cells in\\n Automotive Application Compared with Performance from a Real Vehicle. Energies, č. 8, zv. 14, 2021.
  • Zhang, Kaiyang – Xu, Fengqiu – Xu, Xianze: Observer-based fast nonlinear MPC for multi-DOF maglev positioning\\n system: Theory and experiment. Control Engineering Practice, č. 104860, zv. 114, 2021.
  • Tao, Xin – Zhou, Yonghua – Zhang, Zhenlin: Model predictive control of a magnetic levitation system using two-level state feedback. Measurement & Control, č. 5-6, zv. 53, str. 962-970, 2020.
  • 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.
  • 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, zv. 8, str. 116704-116723, 2020.
  • Horvathova, Michaela – Oravec, Juraj – Bakosova, Monika: Real-Time Convex-lifting-based Robust Control Using Approximated Control Law. V 2020 59th IEEE Conference on Decision and Control (CDC), str. 2160-2165, 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.
  • La Delfa, S. – Enjalbert, S. – Polet, P. – Vanderhaegen, F.: Design of a cooperative eco-driving rail control system: an experimental study. Cognition Technology & Work, č. 2, SI, zv. 23, str. 285-297, 2021.
  • Takacs, Gergely – Mihalik, Jakub – Mikulas, Erik – Gulan, Martin: MagnetoShield: Prototype of a Low-Cost Magnetic Levitation Device for Control Education. V Proceedings of the 2020 IEEE Global Engineering Education Conference (educon 2020), str. 1516-1525, 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, zv. 91, str. 37-49, 2020.
  • Yfoulis, Christos – Papadopoulou, Simira – Voutetakis, Spyridon: Enhanced control of a buck-boost DC-DC converter via a closed-form MPC reference governor scheme. V 45th Annual Conference of the IEEE Industrial Electronics Society (iecon 2019), str. 365-370, 2019.
  • Molina, Luis Miguel Castellanos – Galluzzi, Renato – Bonfitto, Angelo – Tonoli, Andrea – Amati, Nicola: Magnetic Levitation Control Based on Flux Density and Current Measurement. Applied Sciences-basel, č. 12, zv. 8, 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.
  • Findeisen, Rolf – Graichen, Knut – Monnigmann, Martin: Embedded optimization in control: an introduction, opportunities, and challenges. At-automatisierungstechnik, č. 11, zv. 66, str. 877-902, 2018.
  • Bonfitto, Angelo – Molina, Luis Miguel Castellanos – Tonoli, Andrea – Amati, Nicola: Offset-Free Model Predictive Control for Active Magnetic Bearing Systems. Actuators, č. 3, zv. 7, 2018.
  • Berner, Patrik Simon – Moennigmann, Martin: Event-Based Networked Model Predictive Control With Overclocked Local Nodes. V 2018 European Control Conference (ECC), str. 306-311, 2018.
  • Raha, Arnab – Chakrabarty, Ankush – Raghunathan, Vijay – Buzzard, Gregery T.: Ultrafast Embedded Explicit Model Predictive Control for Nonlinear Systems. V 2017 American Control Conference (ACC), str. 4398-4403, 2017.
J. Drgoňa – M. Klaučo – F. Janeček – M. Kvasnica: Optimal control of a laboratory binary distillation column via regionless explicit MPC. Computers & Chemical Engineering, zv. 96, str. 139–148, 2017.
  • Počet citácií       11
  • Ramezani, M.H. – Sadati, N.: Hierarchical optimal control of a binary distillation column. Optimal Control Applications and Methods, 2018.
  • Ahmadian Behrooz, H.: Robust set-point optimization of inferential control system of crude oil distillation units. ISA Transactions, 2019.
  • Ramezani, Mohammad Hossein – Sadati, Nasser: Hierarchical optimal control of a binary distillation column. Optimal Control Applications & Methods, č. 1, zv. 40, str. 172-185, 2019.
  • Bayram, Ismail – Hapoglu, Hale – Aldemir, Adnan: Impact of Robust Error Control on Fluid Level by Wireless Network Applications. Journal of Polytechnic-politeknik Dergisi, č. 3, zv. 21, str. 685-691, 2018.
  • Behrooz, Hesam Ahmadian: Robust set-point optimization of inferential control system of crude oil distillation units. Isa Transactions, zv. 95, str. 93-109, 2019.
  • Katz, Justin – Burnak, Baris – Pistikopoulos, Efstratios N.: A space exploration algorithm for multiparametric programming via Delaunay triangulation. Optimization and Engineering, 2020.
  • Jeong, M. – Fuchs, S. – Biela, J.: When FPGAs Meet Regionless Explicit MPC: An Implementation of Long-horizon Linear MPC for Power Electronic Systems. V IECON Proceedings (Industrial Electronics Conference), str. 3085-3092, 2020.
  • Burnak, B. – Katz, J. – Pistikopoulos, E.N.: A space exploration algorithm for multiparametric programming via Delaunay triangulation. Optimization and Engineering, č. 1, zv. 22, str. 555-579, 2021.
  • Gilimalage, A.S.M. – Kimura, S.: Model predictive control-based control algorithm for a target-chaser maneuvering situation. Advanced Robotics, č. 21-22, zv. 35, str. 1265-1276, 2021.
  • Theunissen, J. – Tota, A. – Gruber, P. – Dhaens, M. – Sorniotti, A.: Preview-based techniques for vehicle suspension control: a state-of-the-art review. Annual Reviews in Control, zv. 51, str. 206-235, 2021.
  • J. Oravec – M. Bakošová – P. Valiauga: Advanced Process Control Design for a Distillation Column Using UniSim Design. Editor(i): M. Fikar and M. Kvasnica, V Proceedings of the 21st International Conference on Process Control, Slovak Chemical Library, Štrbské Pleso, Slovakia, str. 303–308, 2017.
M. Klaučo – M. Kvasnica: Control of a boiler-turbine unit using MPC-based reference governors. Applied Thermal Engineering, zv. 110, str. 1437–1447, 2017.
  • Počet citácií       39
  • Fan Zhang – Xiao Wu – Jiong Shen: Extended state observer based fuzzy model predictive control for ultra-supercritical boiler-turbine unit. Applied Thermal Engineering, zv. 118, str. 90 - 100, 2017.
  • Emre ÖZKOP – İsmail Hakkı ALTAŞ: Performance of PSO Based Classical and Intelligent Controllers for Water Level Control of a Steam Generator. Journal of Science and Engineering, č. 57, zv. 19, str. 835, 2017.
  • Gholamreza Vossoughi – Hamed Moradi – Soheil Ghabraei: Design & application of adaptive variable structure & H∞ robust optimal schemes in nonlinear control of boiler-turbine unit in the presence of various uncertainties. Energy, 2017.
  • Zhuo, X. – Lou, C. – Zhou, H. – Zhuo, J. – Fu, P.: Hierarchical Takagi-Sugeno fuzzy hyperbolic tangent static model control for a circulating fluidized bed boiler thermal power unit. Energy, zv. 162, str. 910-917, 2018.
  • Oravec, J. – Bakošová, M. – Trafczynski, M. – Vasičkaninová, A. – Mészáros, A. – Markowski, M.: Robust model predictive control and PID control of shell-and-tube heat exchangers. Energy, zv. 159, str. 1-10, 2018.
  • Wang, D. – Zhou, Y. – Li, X.: A dynamic model used for controller design for fast cut back of coal-fired boiler-turbine plant. Energy, zv. 144, str. 526-534, 2018.
  • Oravec, J. – Bakošová, M. – Vasičkaninová, A. – Mészáros, A.: Robust model predictive control of a plate heat exchanger. Chemical Engineering Transactions, zv. 70, str. 25-30, 2018.
  • Ghabraei, S. – Moradi, H. – Vossoughi, G.: Design & application of adaptive variable structure & H∞ robust optimal schemes in nonlinear control of boiler-turbine unit in the presence of various uncertainties. Energy, zv. 142, str. 1040-1056, 2018.
  • Pina, W. – Feliu-Batlle, V. – Rivas-Perez, R.: Direct continuous-Time system identification of the purification process of the nimotuzumab, a humanized monoclonal antibody. IEEE Latin America Transactions, č. 1, zv. 16, str. 31-37, 2018.
  • Zhang, F. – Zhang, Y. – Wu, X. – Shen, J. – Lee, K.Y.: Control of ultra-supercritical once-through boiler-turbine unit using MPC and ESO approaches. V 1st Annual IEEE Conference on Control Technology and Applications, CCTA 2017, str. 994-999, 2017.
  • Oravec, J. – Trafczynski, M. – Bakošová, M. – Markowski, M. – Mészáros, A. – Urbaniec, K.: Robust model predictive control of heat exchanger network in the presence of fouling. Chemical Engineering Transactions, zv. 61, str. 337-342, 2017.
  • Zhou, Y. – Wang, D.: An improved coordinated control technology for coal-fired boiler-turbine plant based on flexible steam extraction system. Applied Thermal Engineering, zv. 125, str. 1047-1060, 2017.
  • Spinelli, Stefano – Farina, Marcello – Ballarino, Andrea: A hierarchical optimization-based scheme for combined Fire-tube Boiler/CHP generation units. V 2018 European Control Conference (ECC), str. 416--421, 2018.
  • Zhu, J. – Wu, X. – Shen, J.: Practical disturbance rejection control for boiler-turbine unit with input constraints. Applied Thermal Engineering, č. 114184, zv. 161, 2019.
  • Niu, Y. – Du, M. – Ge, W. – Luo, H. – Zhou, G.: A dynamic nonlinear model for a once-through boiler-turbine unit in low load. Applied Thermal Engineering, č. 113880, zv. 161, 2019.
  • Hultgren, M. – Ikonen, E. – Kovács, J.: Integrated control and process design for improved load changes in fluidized bed boiler steam path. Chemical Engineering Science, zv. 199, str. 164-178, 2019.
  • Li, X. – Fu, J. – Cao, H.: Simulation study on fuzzy state variable-mpc of coal- fired power plants. V ACM International Conference Proceeding Series, str. 91-95, 2019.
  • P.U., S. – Desai, K. – Barve, J. – Nataraj, P.S.V.: An experimental case study of robust cascade two-element control of boiler drum level. ISA Transactions, 2019.
  • Wang, G. – Wu, J. – Ma, X.: A nonlinear state-feedback state-feedforward tracking control strategy for a boiler-turbine unit. Asian Journal of Control, 2019.
  • Li, Xiaoming – Fu, Junfeng – Cao, Hong: Simulation Study on Fuzzy State Variable-MPC of Coal-Fired Power Plants. V Proceedings of the 11th International Conference on Computer Modeling and Simulation (iccms 2019) and 8th International Conference on Intelligent Computing and Applications (icica 2019), str. 91-95, 2019.
  • Spinelli, Stefano – Farina, Marcello – Ballarino, Andrea: An optimal hierarchical control scheme for smart generation units: An application to combined steam and electricity generation. Journal of Process Control, zv. 94, str. 58-74, 2020.
  • Du, Ming – Niu, Yuguang – Li, Hong – Zhou, Zhenhua – Zhang, Jingxiang – Jiang, Xiaotao – Liu, Rui: The control-oriented model of coordinated control system based on stochastic differential equations. Energy Science & Engineering, 2020.
  • Wang, Guoxu – Wu, Jie – Ma, Xiaoqian: A nonlinear state-feedback state-feedforward tracking control strategy for a boiler-turbine unit. Asian Journal of Control, č. 5, zv. 22, str. 2004-2016, 2020.
  • Fan, He – Su, Zhi-gang – Wang, Pei-hong – Lee, Kwang Y.: A dynamic mathematical model for once-through boiler-turbine units with superheated steam temperature. Applied Thermal Engineering, č. 114912, zv. 170, 2020.
  • Sunil, P. U. – Desai, Khushali – Barve, Jayesh – Nataraj, P. S. V.: An experimental case study of robust cascade two-element control of boiler drum level. Isa Transactions, zv. 96, str. 337-351, 2020.
  • Niu, Yuguang – Du, Ming – Ge, Weichun – Luo, Huanhuan – Zhou, Guiping: A dynamic nonlinear model for a once-through boiler-turbine unit in low load. Applied Thermal Engineering, č. 113880, zv. 161, 2019.
  • Zhu, Jianzhong – Wu, Xiao – Shen, Jiong: Practical disturbance rejection control for boiler-turbine unit with input constraints. Applied Thermal Engineering, č. 114184, zv. 161, 2019.
  • Hultgren, Matias – Ikonen, Enso – Kovacs, Jeno: Integrated control and process design for improved load changes in fluidized bed boiler steam path. Chemical Engineering Science, zv. 199, str. 164-178, 2019.
  • Efremova, T. – Shchegolev, S.: Boiler drum automatic power management system. V E3S Web of Conferences, 2020.
  • Spinelli, S. – Farina, M. – Ballarino, A.: An optimal hierarchical control scheme for smart generation units: An application to combined steam and electricity generation. Journal of Process Control, zv. 94, str. 58-74, 2020.
  • Wang, G. – Wu, J. – Ma, X.: A nonlinear state-feedback state-feedforward tracking control strategy for a boiler-turbine unit. Asian Journal of Control, č. 5, zv. 22, str. 2004-2016, 2020.
  • Fan, H. – Su, Z.-G. – Wang, P.-H. – Lee, K.Y.: A dynamic mathematical model for once-through boiler-turbine units with superheated steam temperature. Applied Thermal Engineering, č. 114912, zv. 170, 2020.
  • Furka, M. – Kis, K. – Horvathova, M. – Mojto, M. – Bakosova, M.: Identification and control of a cascade of biochemical reactors. V Proceedings of the 30th International Conference on Cybernetics and Informatics, K and I 2020, 2020.
  • Sunil, P.U. – Desai, K. – Barve, J. – Nataraj, P.S.V.: An experimental case study of robust cascade two-element control of boiler drum level. ISA Transactions, zv. 96, str. 337-351, 2020.
  • Du, M. – Niu, Y. – Li, H. – Zhou, Z. – Zhang, J. – Jiang, X. – Liu, R.: The control-oriented model of coordinated control system based on stochastic differential equations. Energy Science and Engineering, 2020.
  • Zhu, J. – Cui, X. – Ni, W.: Model predictive control based control strategy for battery energy storage system integrated power plant meeting deep load peak shaving demand. Journal of Energy Storage, č. 103811, zv. 46, 2022.
  • Liu, Y. – Gao, A. – Wei, Q.: Optimal Tracking Control of the Boiler-turbine System Based on Adaptive Dynamic Programming. V Proceedings of the International Joint Conference on Neural Networks, 2021.
  • Vrlić, M. – Ritzberger, D. – Jakubek, S.: 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.
  • Du, M. – Niu, Y. – Li, H. – Zhou, Z. – Zhang, J. – Jiang, X. – Liu, R.: The control-oriented model of coordinated control system based on stochastic differential equations. Energy Science and Engineering, č. 1, zv. 9, str. 129-141, 2021.
M. Klaučo – S. Blažek – M. Kvasnica: An Optimal Path Planning Problem for Heterogeneous Multi-Vehicle Systems. International Journal of Applied Mathematics and Computer Science, č. 2, zv. 26, str. 297–308, 2016.
  • Počet citácií       13
  • Cera, M. – Fedriani, E.M.: An advance in infinite graph models for the analysis of transportation networks. International Journal of Applied Mathematics and Computer Science, č. 4, zv. 26, str. 855-870, 2016.
  • Ha, J.-S. – Choi, H.-L. – Jeon, J.H.: Iterative methods for efficient sampling-based optimal motion planning of nonlinear systems. International Journal of Applied Mathematics and Computer Science, č. 1, zv. 28, str. 155-168, 2018.
  • Adacher, L. – Gemma, A.: A robust algorithm to solve the signal setting problem considering different traffic assignment approaches. International Journal of Applied Mathematics and Computer Science, č. 4, zv. 27, str. 815-826, 2017.
  • Sullivan, N. – Grainger, S. – Cazzolato, B.: Robust heterogeneous multi-robot routing for low-intelligence agents. V Australasian Conference on Robotics and Automation, ACRA, str. 287-295, 2017.
  • Liu, Xiao-Huan – Zhang, De-Gan – Yan, Hao-Ran – Cui, Yu-Ya – Chen, Lu: A New Algorithm of the Best Path Selection Based on Machine Learning. IEEE ACCess, zv. 7, str. 126913-126928, 2019.
  • Jafarzadeh, Hassan – Fleming, Cody H.: AN EXACT GEOMETRY-BASED ALGORITHM FOR PATH PLANNING. International Journal of Applied Mathematics and Computer Science, č. 3, zv. 28, str. 493-504, 2018.
  • Ha, Jung-Su – Choi, Han-Lim – Jeon, Jeong Hwan: ITERATIVE METHODS FOR EFFICIENT SAMPLING-BASED OPTIMAL MOTION PLANNING OF NONLINEAR SYSTEMS. International Journal of Applied Mathematics and Computer Science, č. 1, zv. 28, str. 155-168, 2018.
  • Adacher, Ludovica – Gemma, Andrea: A ROBUST ALGORITHM TO SOLVE THE SIGNAL SETTING PROBLEM CONSIDERING DIFFERENT TRAFFIC ASSIGNMENT APPROACHES. International Journal of Applied Mathematics and Computer Science, č. 4, zv. 27, str. 815-826, 2017.
  • Cera, Martin – Fedriani, Eugenio M.: AN ADVANCE IN INFINITE GRAPH MODELS FOR THE ANALYSIS OF TRANSPORTATION NETWORKS. International Journal of Applied Mathematics and Computer Science, č. 4, zv. 26, str. 855-869, 2016.
  • Chen, M. – Chen, Y. – Chen, Z. – Yang, Y.: Path planning of UAV-UGV heterogeneous robot system in road network. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), zv. 11745 LNAI, str. 497-507, 2019.
  • Khaksar, W. – Uddin, M.Z. – Torresen, J.: Multiquery Motion Planning in Uncertain Spaces: Incremental Adaptive Randomized Roadmaps. International Journal of Applied Mathematics and Computer Science, č. 4, zv. 29, str. 641-654, 2020.
  • Sun, K. – Liu, X.: Path Planning for an Autonomous Underwater Vehicle in a Cluttered Underwater Environment Based on the Heat Method. International Journal of Applied Mathematics and Computer Science, č. 2, zv. 31, str. 289-301, 2021.
  • Chen, Y. – Chen, M. – Chen, Z. – Cheng, L. – Yang, Y. – Li, H.: Delivery path planning of heterogeneous robot system under road network constraints. Computers and Electrical Engineering, č. 107197, zv. 92, 2021.
M. Jelemenský – M. Klaučo – R. Paulen – J. Lauwers – F. Logist – J. Van Impe – M. Fikar: Time-Optimal Control and Parameter Estimation of Diafiltration Processes in the Presence of Membrane Fouling. V 11th IFAC Symposium on Dynamics and Control of Process Systems, including Biosystems, zv. 11, str. 242–247, 2016.
  • Počet citácií       2
  • Krewer, U. – Scholl, S. – Rehbein, M. – Xie, X. – Schenkendorf, R.: The impact of global sensitivities and design measures in model-based optimal experimental design. Processes, č. 4, zv. 6, 2018.
  • Nawaz, Alam – Saxena, Nikita – Arora, Amarpreet Singh – Yun, Choa Mun – Lee, Moonyong: Auto-Tuning of Identified Highly Sensitive Parameters for ANAMMOX System: Advanced Modeling Approach. IEEE Transactions on Industrial Informatics, č. 11, zv. 17, str. 7238-7245, 2021.
J. Drgoňa – M. Klaučo – M. Kvasnica: MPC-Based Reference Governors for Thermostatically Controlled Residential Buildings. V 54th IEEE Conference on Decision and Control, Osaka, Japan, zv. 54, 2015.
  • Počet citácií       6
  • Lomas, K.J. – Oliveira, S. – Warren, P. – Haines, V.J. – Chatterton, T. – Beizaee, A. – Prestwood, E. – Gething, B.: Do domestic heating controls save energy? A review of the evidence. Renewable and Sustainable Energy Reviews, zv. 93, str. 52-75, 2018.
  • Baumeister, Alexander – Schaefer, C.: Design of an Online Optimisation Tool for Smart Home Heating Control. 2018.
  • Adam, Martin – Pecorelli, Mario – others: Recommendations in Augmented Reality Applications-the Effect of Customer Reviews and Seller Recommendations on Purchase Intention and Product Selection. 2018.
  • Short, Michael – Rodriguez, Sergio – Charlesworth, Richard – Crosbie, Tracey – Dawood, Nashwan: Optimal Dispatch of Aggregated HVAC Units for Demand Response: An Industry 4.0 Approach. Energies, č. 22, zv. 12, 2019.
  • Arroyo, J. – Manna, C. – Spiessens, F. – Helsen, L.: Reinforced model predictive control (RL-MPC) for building energy management. Applied Energy, č. 118346, zv. 309, 2022.
  • Huchuk, B. – Sanner, S. – O\\\'Brien, W.: Development and evaluation of data-driven controls for residential smart thermostats. Energy and Buildings, č. 111201, zv. 249, 2021.
M. Nehéz – D. Bernát – M. Klaučo: Comparison of Algorithms for Near-Optimal Dominating Sets Computation in Real-World Networks. Editor(i): B. Rachev, A. Smrikarov, V Proceedings of the 16th International Conference on Computer Systems and Technologies, Association for Computing Machinery (ACM), Dublin, Ireland, str. 199–206, 2015.
  • Počet citácií       4
  • David Chalupa: An order-based algorithm for minimum dominating set with application in graph mining. Information Sciences, č. Supplement C, zv. 426, str. 101 - 116, 2018.
  • Li, R. – Hu, S. – Liu, H. – Li, R. – Ouyang, D. – Yin, M.: Multi-start local search algorithm for the minimum connected dominating set problems. Mathematics, č. 12, zv. 7, 2019.
  • Pethaperumal, D. – Peng, Y. – Qin, H.: Budget-Hub: A low cost IoT hub selection and neighbor assignment scheme. V IEEE World Forum on Internet of Things, WF-IoT 2018 - Proceedings, str. 700-705, 2018.
  • Adel A. Alofairi – Rashad Ismail – Ibrahim E. Elsemman – Eamad Mabrouk: COMPARATIVE ANALYSIS OF INTEGER LINEAR PROGRAMMING AND META-HEURISTIC METHODS FOR MINIMUM DOMINATING SET PROBLEM. Assiut University Journal of Mathematics and Computer Science, č. 1, zv. 1, str. 1-20, 2019.
J. Oravec – M. Klaučo – M. Kvasnica – J. Löfberg: Optimal Vehicle Routing with Interception of Targets’ Neighbourhoods. V European Control Conference 2015, Linz, Austria, str. 2538–2543, 2015.
  • Počet citácií       3
  • Chen, Y. – Tan, Y. – Cheng, L. – Wu, H.: Path planning for a heterogeneous aerial-ground robot system with neighbourhood constraints. Jiqiren/Robot, č. 1, zv. 39, str. 1-7, 2017.
  • Chen, Y. – Ren, S. – Chen, Z. – Chen, M. – Wu, H.: Path planning for vehicle-borne system consisting of multi air-ground robots. Robotica, č. 3, zv. 38, str. 493-511, 2020.
  • Chen, M. – Chen, Y. – Chen, Z. – Yang, Y.: Path planning of UAV-UGV heterogeneous robot system in road network. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), zv. 11745 LNAI, str. 497-507, 2019.
J. Drgoňa – M. Klaučo – R. Valo – J. Bendžala – M. Fikar: Model Identification and Predictive Control of a Laboratory Binary Distillation Column. Editor(i): M. Fikar and M. Kvasnica, V Proceedings of the 20th International Conference on Process Control, Slovak Chemical Library, Štrbské Pleso, Slovakia, 2015.
  • Počet citácií       4
  • J. Oravec – M. Bakošová – P. Artzová: Advanced process control design for a distillation column using UniSim design. V 21st International Conference on Process Control (PC), str. 303-308, 2017.
  • Orjuela Rojas, A. – Sandoval, O.L.R. – Hurtado, D.A.: PID control for distilled product and bottom concentration in a binary distillation column. International Review of Mechanical Engineering, č. 4, zv. 11, str. 242-248, 2017.
  • Castelblanco, J.S.U. – Rojas, A.O. – Hurtado, D.A.: MIMO MPC control of distillate and background concentration to binary distillation column in discrete state space. International Review of Automatic Control, č. 6, zv. 9, str. 348-354, 2016.
  • Mohammad, N.N. – Azman, A.A. – Marzaki, M.H. – Adnan, R. – Tajjudin, M. – Fazalul Rahiman, M.H. – Tajuddin, S.N.: Performance comparison and energy consumption index between MPC and FuzzyPID in small-scaled agarwood distillation pot. V 2018 9th IEEE Control and System Graduate Research Colloquium, ICSGRC 2018 - Proceeding, str. 156-161, 2019.
M. Kalúz – M. Klaučo – M. Kvasnica: Real-Time Implementation of a Reference Governor on the Arduino Microcontroller. Editor(i): M. Fikar and M. Kvasnica, V Proceedings of the 20th International Conference on Process Control, Slovak Chemical Library, Štrbské Pleso, Slovakia, str. 350–356, 2015.
  • Počet citácií       6
  • Spiewak, A. – Salabun, W: A Mobile Gas Detector with an Arduino Microcontroller. International Journal of Computer Technology and Applications, č. 4, zv. 6, str. 636-641, 2015.
  • Zavitsanou, S. – Chakrabarty, A. – Dassau, E. – Doyle III, F.J.: Embedded Control in Wearable Medical Devices: Application to the Artificial Pancreas. Processes, č. 4, zv. 4, 2016.
  • Bonfitto, A. – Molina, L.M.C. – Tonoli, A. – Amati, N.: Offset-free model predictive control for active magnetic bearing systems. Actuators, č. 3, zv. 7, 2018.
  • 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.
  • Š. Chamraz – K. Žáková: Control of CE152 Magnetic Levitation: Linearly Changing Control Signal. V 2020 Cybernetics Informatics (K I), str. 1-7, 2020.
  • Bengtsson, Lars: Implementation of Control Algorithms in Small Embedded Systems. Engineering, č. 9, zv. 12, str. 623–639, 2020.
M. Klaučo – M. Kvasnica: Explicit MPC Approach to PMV-Based Thermal Comfort Control. V 53rd IEEE Conference on Decision and Control, Los Angeles, California, USA, zv. 53, str. 4856–4861, 2014.
  • Počet citácií       14
  • Katsigarakis, K.I. – Kontes, G.D. – Giannakis, G.I. – Rovas, D.V.: Sense-Think-Act Framework for Intelligent Building Energy Management. Computer-Aided Civil and Infrastructure Engineering, č. 1, zv. 31, str. 50-64, 2016.
  • Serale,G. – Fiorentini,M. – Capozzoli,A. – Bernardini,D. – Bemporad,A.: Model Predictive Control (MPC) for enhancing building and HVAC system energy efficiency: Problem formulation, applications and opportunities. Energies, č. 3, zv. 11, 2018.
  • Kontes, G.D. – Giannakis, G.I. – Sánchez, V. – de Agustin-Camacho, P. – Romero-Amorrortu, A. – Panagiotidou, N. – Rovas, D.V. – Steiger, S. – Mutschler, C. – Gruen, G.: Simulation-based evaluation and optimization of control strategies in buildings. Energies, č. 12, zv. 11, 2018.
  • Park, J.Y. – Nagy, Z.: Comprehensive analysis of the relationship between thermal comfort and building control research - A data-driven literature review. Renewable and Sustainable Energy Reviews, zv. 82, str. 2664-2679, 2018.
  • Schmidt, Mischa – Ahlund, Christer: Smart buildings as Cyber-Physical Systems: Data-driven predictive control strategies for energy efficiency. Renewable & Sustainable Energy Reviews, zv. 90, str. 742-756, 2018.
  • Carli, R. – Cavone, G. – Dotoli, M. – Epicoco, N. – Scarabaggio, P.: Model predictive control for thermal comfort optimization in building energy management systems. V Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, str. 2608-2613, 2019.
  • Yang, Shiyu – Wan, Man Pun – Chen, Wanyu – Ng, Bing Feng – Dubey, Swapnil: Model predictive control with adaptive machine-learning-based model for building energy efficiency and comfort optimization. Applied Energy, č. 115147, zv. 271, 2020.
  • Kim, Joowook – Song, Doosam – Kim, Suyeon – Park, Sohyun – Choi, Youngjin – Lim, Hyunwoo: Energy-Saving Potential of Extending Temperature Set-Points in a VRF Air-Conditioned Building. Energies, č. 9, zv. 13, 2020.
  • Ngarambe, Jack – Yun, Geun Young – Santamouris, Mat: The use of artificial intelligence (AI) methods in the prediction of thermal comfort in buildings: energy implications of AI-based thermal comfort controls. Energy and Buildings, č. 109807, zv. 211, 2020.
  • Carli, Raffaele – Cavone, Graziana – Ben Othman, Sarah – Dotoli, Mariagrazia: IoT Based Architecture for Model Predictive Control of HVAC Systems in Smart Buildings. Sensors, č. 3, zv. 20, 2020.
  • Kostka, M. – Zajac, A.: The Impact of Climate Change on Primary Air Treatment Processes and Energy Demand in Air Conditioning Systems—A Case Study from Warsaw, Poland. Energies, č. 1, zv. 15, 2022.
  • Yang, Y. – Hu, G. – Spanos, C.J.: Stochastic Optimal Control of HVAC System for Energy-Efficient Buildings. IEEE Transactions on Control Systems Technology, č. 1, zv. 30, str. 376-383, 2022.
  • Prince – Hati, A.S.: A comprehensive review of energy-efficiency of ventilation system using Artificial Intelligence. Renewable and Sustainable Energy Reviews, č. 111153, zv. 146, 2021.
  • Vinnarasi, A. – Sangeetha, M.: Comfort Cognitive IoT for Efficient Monitoring and Predictive in Building Management Systems. V 2021 International Conference on Wireless Communications, Signal Processing and Networking, WiSPNET 2021, str. 345-349, 2021.
M. Klaučo – J. Drgoňa – M. Kvasnica – S. Di Cairano: Building Temperature Control by Simple MPC-like Feedback Laws Learned from Closed-Loop Data. V Preprints of the 19th IFAC World Congress Cape Town (South Africa) August 24 - August 29, 2014, str. 581–586, 2014.
  • Počet citácií       9
  • Thieblemont Hélène – Haghighat Fariborz – Ooka Ryozo – Moreau Alain: Predictive Control Strategies based on Weather Forecast in Buildings with Energy Storage System: A Review of the State-of-the Art. Energy and Buildings, 2017.
  • Carli, R. – Cavone, G. – Dotoli, M. – Epicoco, N. – Scarabaggio, P.: Model predictive control for thermal comfort optimization in building energy management systems. V Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, str. 2608-2613, 2019.
  • Wang, J. – Li, S. – Chen, H. – Yuan, Y. – Huang, Y.: Data-driven model predictive control for building climate control: Three case studies on different buildings. Building and Environment, č. 106204, zv. 160, 2019.
  • Wang, X. – Liu, Y. – Xu, L. – Liu, J. – Sun, H.: A chance-constrained stochastic model predictive control for building integrated with renewable resources. Electric Power Systems Research, č. 106348, zv. 184, 2020.
  • Carli, R. – Cavone, G. – Othman, S.B. – Dotoli, M.: IoT based architecture for model predictive control of HVAC systems in smart buildings. Sensors (Switzerland), č. 3, zv. 20, 2020.
  • Lee, D.-S. – Chen, Y.-T. – Chao, S.-L.: Universal workflow of artificial intelligence for energy saving. Energy Reports, zv. 8, str. 1602-1633, 2022.
  • Ma, S. – Zou, Y. – Li, S.: Coordinated control for Air Handling Unit and Variable Air Volume boxes in multi-zone HVAC system. Journal of Process Control, zv. 107, str. 17-26, 2021.
  • Yang, S. – Wan, M.P. – Chen, W. – Ng, B.F. – Dubey, S.: Experiment study of machine-learning-based approximate model predictive control for energy-efficient building control. Applied Energy, č. 116648, zv. 288, 2021.
  • Vinnarasi, A. – Sangeetha, M.: Comfort Cognitive IoT for Efficient Monitoring and Predictive in Building Management Systems. V 2021 International Conference on Wireless Communications, Signal Processing and Networking, WiSPNET 2021, str. 345-349, 2021.
M. Klaučo – S. Blažek – M. Kvasnica – M. Fikar: Mixed-Integer SOCP Formulation of the Path Planning Problem for Heterogeneous Multi-Vehicle Systems. V European Control Conference 2014, Strasbourg, France, str. 1474–1479, 2014.
  • Počet citácií       10
  • Gambella, C.: Mathematical Optimization for Routing and Logistic Problems. 2016.
  • Chen, Y. – Tan, Y. – Cheng, L. – Wu, H.: Path planning for a heterogeneous aerial-ground robot system with neighbourhood constraints. Jiqiren/Robot, č. 1, zv. 39, str. 1-7, 2017.
  • Lodi, Andrea – Gambella, Claudio – Vigo, Daniele: Exact Solutions for the Carrier--Vehicle Traveling Salesman Problem. Transportation Science, zv. 52, str. 320-330, 2018.
  • Chen, Yang – Ren, Shiwen – Chen, Zhihuan – Chen, Mengqing – Wu, Huaiyu: Path Planning for Vehicle-borne System Consisting of Multi Air-ground Robots. Robotica, č. 3, zv. 38, str. 493-511, 2020.
  • Chen, M. – Chen, Y. – Chen, Z. – Yang, Y.: Path planning of UAV-UGV heterogeneous robot system in road network. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), zv. 11745 LNAI, str. 497-507, 2019.
  • Ren, S. – Chen, Y. – Xiong, L. – Chen, Z. – Chen, M.: Path Planning for the Marsupial double-UAVs System in Air-ground Collaborative Application. V Chinese Control Conference, CCC, str. 5420-5425, 2018.
  • Gambella, C. – Lodi, A. – Vigoa, D.: Exact solutions for the carrier-vehicle traveling salesman problem. Transportation Science, č. 2, zv. 52, str. 320-330, 2018.
  • Fahradyan, T. – Bono Rossello, N. – Garone, E.: Multiple Carrier-Vehicle Travelling Salesman Problem. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), zv. 11995 LNCS, str. 180-189, 2020.
  • Erdogan, Gunes – Yildirim, E. Alper: Exact and Heuristic Algorithms for the Carrier-Vehicle Traveling Salesman Problem. Transportation Science, č. 1, zv. 55, str. 101-121, 2021.
  • Larasati, M.R. – Wang, I.-L.: An integrated integer programming model with a simulated annealing heuristic for the carrier vehicle traveling salesman problem. V Procedia Computer Science, str. 301-308, 2021.
J. Drgoňa – M. Kvasnica – M. Klaučo – M. Fikar: Explicit Stochastic MPC Approach to Building Temperature Control. V IEEE Conference on Decision and Control, Florence, Italy, str. 6440–6445, 2013.
  • Počet citácií       15
  • Farina, M. – Giulioni, L. – Scattolini, R.: Stochastic linear Model Predictive Control with chance constraints – A review. Journal of Process Control, zv. 44, str. 53-67, 2016.
  • Parisio, A. – Fabietti, L. – Molinari, M. – Varagnolo, D. – Johansson, K.H.: Control of HVAC systems via scenario-based explicit MPC. V Decision and Control (CDC), 2014 IEEE 53rd Annual Conference on, str. 5201-5207, 2014.
  • Lesic, V. – Vasak, M. – Martincevic, A. – Gulin, M. – Starcic, A. – Novak, H.: Computer-Assisted Management of Building Climate and Microgrid with Model Predictive Control. International Journal of Computer, Electrical, Automation, Control and Information Engineering, č. 10, zv. 9, str. 2036-2047, 2015.
  • A. Mesbah: Stochastic Model Predictive Control: An Overview and Perspectives for Future Research. IEEE Control Systems, č. 6, zv. 36, str. 30-44, 2016.
  • Kontes, Georgios D. – Giannakis, Georgios I. – Horn, Philip – Steiger, Simone – Rovas, Dimitrios V.: Using Thermostats for Indoor Climate Control in Office Buildings: The Effect on Thermal Comfort. Energies, č. 9, zv. 10, 2017.
  • Vogler-Finck, P. J. C. – Wisniewski, R. – Popovski, P.: Reducing the carbon footprint of house heating through model predictive control - A simulation study in Danish conditions. Sustainable Cities and Society, zv. 42, str. 558-573, 2018.
  • Ławryńczuk, M. – Ocłoń, P.: Model Predictive Control and energy optimisation in residential building with electric underfloor heating system. Energy, str. 1028-1044, 2019.
  • Wang, R. – Bao, J.: Advanced-step Stochastic Model Predictive Control using Random Forests. V Proceedings of the IEEE Conference on Decision and Control, str. 3283-3287, 2019.
  • Qin, D. – Qin, L.: Energy Management Strategy for a Power-split Hybrid Electric Vehicle Based on Explicit Stochastic Model Predictive Control [基于显式随机模型预测控制的功率分流式混合动力车辆能量管理策略]. Huanan Ligong Daxue Xuebao/Journal of South China University of Technology (Natural Science), č. 7, zv. 47, str. 112-120, 2019.
  • Valenzuela, Patricio E. – Ebadat, Afrooz – Everitt, Niklas – Parisio, Alessandra: Closed-Loop Identification for Model Predictive Control of HVAC Systems: From Input Design to Controller Synthesis. IEEE Transactions on Control Systems Technology, č. 5, zv. 28, str. 1681-1695, 2020.
  • Kumar, Ranjeet – Wenzel, Michael J. – ElBsat, Mohammad N. – Risbeck, Michael J. – Drees, Kirk H. – Zavala, Victor M.: Stochastic model predictive control for central HVAC plants. Journal of Process Control, zv. 90, str. 1-17, 2020.
  • Lee, Zachary E. – Zhang, K. Max: Generalized reinforcement learning for building control using Behavioral Cloning. Applied Energy, č. 117602, zv. 304, 2021.
  • Ceha, T.J. – De Araujo Passos, L.A. – Baldi, S. – De Schutter, B.: Model predictive control for optimal integration of a thermal chimney and solar shaded building. V 2021 29th Mediterranean Conference on Control and Automation, MED 2021, str. 21-26, 2021.
  • Alqurashi, Amru: The State of the Art in Model Predictive Control Application for Demand Response. Journal of Sustainable Development of Energy Water and Environment Systems-jsdewes, č. 3, zv. 10, 2022.
  • Uytterhoeven, Anke – Van Rompaey, Robbe – Bruninx, Kenneth – Helsen, Lieve: Chance constrained stochastic MPC for building climate control under combined parametric and additive uncertainty. Journal of Building Performance Simulation, č. 3, zv. 15, str. 410-430, 2022.

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