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Citácie

  • Celkový počet citácií       338

J. Drgoňa – K. Kiš – A. Tuor – D. Vrabie – M. Klaučo: Differentiable predictive control: Deep learning alternative to explicit model predictive control for unknown nonlinear systems. Journal of Process Control, zv. 116, str. 80–92, 2022.
  • Počet citácií       3
  • Cai, Panpan – Hsu, David: Closing the Planning-Learning Loop With Application to Autonomous Driving. IEEE Transactions on Robotics, č. 2, zv. 39, str. 998-1011, 2023.
  • Schwung, Andreas – Yuwono, Steve: Model Predictive Control with Adaptive PLC-based Policy on Low Dimensional State Representation for Industrial Applications. V 2023 31st Mediterranean Conference on Control and Automation, Med, str. 883-889, 2023.
  • Walter, Daniel – Vasquez-Varas, Donato – Kunisch, Karl: Learning Optimal Feedback Operators and their Sparse Polynomial Approximations. Journal of Machine Learning Research, č. 301, zv. 24, 2023.
J. OravecM. Klaučo: Real-time tunable approximated explicit MPC. Automatica, zv. 142, str. 110315, 2022.
  • Počet citácií       1
  • Zheng, Yonggui – Liu, Meng – Wu, Hao – Wang, Jun: Analysis of Explicit Model Predictive Control for Track-Following Servo Control of Lunar Gravity Compensation Facility. Applied Sciences-basel, č. 7, zv. 13, 2023.
K. KišM. KlaučoM. Kvasnica: Explicit MPC in the form of Sparse Neural Networks. Editor(i): R. Paulen and M. Fikar, V Proceedings of the 23rd International Conference on Process Control, IEEE, Slovak University of Technology, str. 163–168, 2021.
  • Počet citácií       2
  • Leonow, Sebastian – Dyrska, Raphael – Moennigmann, Martin: Embedded Implementation of a Neural Network emulating Nonlinear MPC in a process control application. V 2023 European Control Conference, ECC, 2023.
  • Shokry, Ahmed – Moulines, Eric: Health-Constrained Explicit Model Predictive Control Based on Deep-Neural Networks Applied to Real-Time Charging of Batteries. Ifac Papersonline, č. 16, zv. 55, str. 142-147, 2022.
D. Efremov – T. Haniš – M. Klaučo: Haptic Driver Guidance for Lateral Driving Envelope Protection Using Model Predictive Control. V IEEE Intelligent Vehicles Symposium, IEEE Xplore, Las Vegas, NV, USA, USA, 2021.
  • Počet citácií       1
  • Noubissie Tientcheu, Simplice Igor – Du, Shengzhi – Djouani, Karim: Review on Haptic Assistive Driving Systems Based on Drivers\\\' Steering-Wheel Operating Behaviour. Electronics, č. 13, zv. 11, 2022.
Y. Lohr – M. KlaučoM. Fikar – M. Mönnigmann: Machine Learning Assisted Solutions of Mixed Integer MPC on Embedded Platforms. Editor(i): Rolf Findeisen, Sandra Hirche, Klaus Janschek, Martin Mönnigmann, V Preprints of the 21st IFAC World Congress (Virtual), Berlin, Germany, July 12-17, 2020, zv. 21, 2020.
  • Počet citácií       4
  • Grelewicz, Patryk – Nowak, Pawel – Khuat, Thanh Tung – Czeczot, Jacek – Klopot, Tomasz – Gabrys, Bogdan: Practical implementation of computationally-efficient machine learning-based control performance assessment system for a class of closed loop systems?. Applied Soft Computing, č. 110690, zv. 146, 2023.
  • Quirynen, Rien – Di Cairano, Stefano: Tailored presolve techniques in branch-and-bound method for fast mixed-integer optimal control applications. Optimal Control Applications & Methods, č. 6, zv. 44, str. 3139-3167, 2023.
  • Decardi-Nelson, B. – You, F.: Optimal energy management in greenhouses using distributed hybrid DRL-MPC framework. Computer Aided Chemical Engineering, zv. 52, str. 1661-1666, 2023.
  • Cauligi, A. – Chakrabarty, A. – Di Cairano, S. – Quirynen, R.: PRISM: Recurrent Neural Networks and Presolve Methods for Fast Mixed-integer Optimal Control. V Proceedings of Machine Learning Research, str. 34-46, 2022.
A. Schirrer – T. Haniš – M. Klaučo – S. Thormann – M. Hromčík – S. Jakubek: Safety-extended Explicit MPC for Autonomous Truck Platooning on Varying Road Conditions. Editor(i): Rolf Findeisen, Sandra Hirche, Klaus Janschek, Martin Mönnigmann, V Preprints of the 21st IFAC World Congress (Virtual), Berlin, Germany, July 12-17, 2020, zv. 21, 2020.
  • Počet citácií       3
  • Thormann, Sebastian – Schirrer, Alexander – Jakubek, Stefan: Safe and Efficient Cooperative Platooning. IEEE Transactions on Intelligent Transportation Systems, č. 2, zv. 23, str. 1368-1380, 2022.
  • Sidorenko, Galina – Thunberg, Johan – Sjoberg, Katrin – Fedorov, Aleksei – Vinci, Alexey: Safety of Automatic Emergency Braking in Platooning. IEEE Transactions on Vehicular Technology, č. 3, zv. 71, str. 2319-2332, 2022.
  • Lyu, Hao – Wang, Ting – Cheng, Rongjun – Ge, Hongxia: Improved longitudinal control strategy for connected and automated truck platoon against cyberattacks. Iet Intelligent Transport Systems, č. 12, SI, zv. 16, str. 1710-1725, 2022.
D. Efremov – M. Klaučo – T. Haniš – M. Hromčík: Driving Envelope Definition and Envelope Protection Using Model Predictive Control. V Proceedings of the American Control Conference, Denver, Colorado, USA, 2020.
  • Počet citácií       3
  • Noubissie Tientcheu, Simplice Igor – Du, Shengzhi – Djouani, Karim: Review on Haptic Assistive Driving Systems Based on Drivers\\\' Steering-Wheel Operating Behaviour. Electronics, č. 13, zv. 11, 2022.
  • Gao, Liming – Beal, Craig – Fescenmyer, Daniel – Brennan, Sean: Analytical Longitudinal Speed Planning for CAVs with Previewed Road Geometry and Friction Constraints. V 2021 IEEE Intelligent Transportation Systems Conference (itsc), str. 1610-1615, 2021.
  • Efremov, Denis – Zhyliaiev, Yehor – Kashel, Bohdan – Hanis, Tomas: Lateral Driving Envelope Protection Using Cascade Control. V 2021 21st International Conference on Control, Automation and Systems (iccas 2021), str. 1440-1446, 2021.
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       10
  • 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.
  • Tsai, Ying-Kuan – Malak, Jr., Richard J.: Design of Approximate Explicit Model Predictive Controller Using Parametric Optimization. Journal of Mechanical Design, č. 12, zv. 144, 2022.
  • Otalora, Pablo – Guzman, Jose Luis – Berenguel, Manuel – Acien, Francisco Gabriel: Data-Driven pH Model in Raceway Reactors for Freshwater and Wastewater Cultures. Mathematics, č. 7, zv. 11, 2023.
  • Furka, Matus – Kis, Karol – Horvathova, Michaela – Mojto, Martin – Bakosova, Monika: Identification and Control of a Cascade of Biochemical Reactors. V Proceedings of the 2020 30th International Conference Cybernetics & Informatics (k&i `20), 2020.
  • Sun, Linjin – Ji, Yangjian – Zhu, Xiaoyang – Peng, Tao: Process knowledge-based random forest regression for model predictive control on a nonlinear production process with multiple working conditions. Advanced Engineering Informatics, č. 101561, zv. 52, 2022.
  • Sitapure, Niranjan – Kwon, Joseph Sang-Il: Neural network-based model predictive control for thin-film chemical deposition of quantum dots using data from a multiscale simulation. Chemical Engineering Research & Design, zv. 183, str. 595-607, 2022.
  • Bedei, Julian – Oberlies, Malte – Schaber, Patrick – Gordon, David – Nuss, Eugen – Li, Liguang – Andert, Jakob: Dynamic measurement with in-cycle process excitation of HCCI combustion: The key to handle complexity of data-driven control?. International Journal of Engine Research, č. 3, zv. 24, str. 1155-1174, 2023.
  • 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.
  • Dutta, Debaprasad – Upreti, Simant R.: Artificial intelligence-based process control in chemical, biochemical, and biomedical engineering. Canadian Journal of Chemical Engineering, č. 11, zv. 99, str. 2467-2504, 2021.
  • Sitapure, Niranjan – Epps, Robert – Abolhasani, Milad – Kwon, Joseph Sang-Il: Multiscale modeling and optimal operation of millifluidic synthesis of perovskite quantum dots: Towards size-controlled continuous manufacturing. Chemical Engineering Journal, č. 127905, zv. 413, 2021.
M. Furka – M. KlaučoM. Kvasnica: Stabilization of Furuta Pendulum using Nonlinear MPC. Research Papers Faculty of Materials Science and Technology in Trnava, č. 45, zv. 27, str. 42–48, 2019.
  • Počet citácií       2
  • Alves, Uiliam Nelson Lendzion Tomaz – Breganon, Ricardo – Pivovar, Luiz Eduardo – de Almeida, Jo{\~a}o Paulo Lima Silva – Barbara, Gustavo Vendrame – Mendon{\c{c}}a, Marcio – Palácios, Rodrigo Henrique Cunha: Discrete-Time H∞ Integral Control Via LMIs Applied to a Furuta Pendulum. Journal of Control, Automation and Electrical Systems, č. 3, zv. 33, str. 1–12, 2022.
  • Homburger, Hannes – Wirtensohn, Stefan – Reuter, Johannes: Swinging up and stabilization control of the Furuta pendulum using model predictive path integral control. V 2022 30th Mediterranean Conference on Control and Automation (MED), str. 7–12, 2022.
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       8
  • 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.
  • Cardoso, Alberto – Oliveira, Paulo Moura – Sa, Joao: Pocket Labs as a STEM Learning Tool and for Engineering Motivation. V Learning in the Age of Digital and Green Transition, Icl2022, Vol 1, str. 413-422, 2023.
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       14
  • 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.
  • Bird, Trevor J. – Jain, Neera – Pangborn, Herschel C. – Koeln, Justin P.: Set-Based Reachability and the Explicit Solution of Linear MPC using Hybrid Zonotopes. V 2022 American Control Conference (ACC), str. 158-165, 2022.
  • Zhang, Yuanjian – Huang, Yanjun – Chen, Zheng – Li, Guang – Liu, Yonggang: An Optimal Control Strategy for Plug-In Hybrid Electric Vehicles Based on Enhanced Model Predictive Control With Efficient Numerical Method. IEEE Transactions on Transportation Electrification, č. 2, zv. 8, str. 2516-2530, 2022.
  • Holaza, Juraj – Oravec, Juraj – Kvasnica, Michal – Dyrska, Raphael – Moennigmann, Martin – Fikar, Miroslav: Accelerating Explicit Model Predictive Control by Constraint Sorting. Ifac Papersonline, č. 2, zv. 53, str. 11356-11361, 2020.
  • Maddalena, E. T. – Moraes, C. G. da S. – Waltrich, G. – Jones, C. N.: A Neural Network Architecture to Learn Explicit MPC Controllers from Data. Ifac Papersonline, č. 2, zv. 53, str. 11362-11367, 2020.
  • Jugade, Chaitanya – Ingole, Deepak – Sonawane, Dayaram – Kvasnica, Michal – Gustafson, John: Memory-Efficient Explicit Model Predictive Control using Posits. V 2019 Sixth Indian Control Conference (icc), str. 188-193, 2019.
  • Zhao, Tong – Yurtsever, Ekim – Paulson, Joel A. – Rizzoni, Giorgio: Formal Certification Methods for Automated Vehicle Safety Assessment. IEEE Transactions on Intelligent Vehicles, č. 1, zv. 8, str. 232-249, 2023.
  • Changizi, Nematollah – Salahshoor, Karim – Siahi, Mehdi: Design and implementation of a sub-optimal explicit mpc using a novel complexity reduction approach based on fuzzy reshaped active regions. International Journal of Dynamics and Control, č. 1, zv. 11, str. 338-353, 2023.
  • Belai, Igor – Huba, Mikulas – Vrancic, Damir: Comparing traditional and constrained disturbance-observer based positional control. Measurement & Control, č. 3-4, zv. 54, str. 170-178, 2021.
  • Cai, Guowei – Jiang, Chao – Yang, Dongfeng – Liu, Xiaojun – Zhou, Shuyu – Cao, Zhichong – Liu, Cheng – Sun, Zhenglong: Data-driven predictive based load frequency robust control of power system with renewables. International Journal of Electrical Power & Energy Systems, č. 109429, zv. 154, 2023.
  • Changizi, Nematollah – Salahshoor, Karim – Siahi, Mehdi: Complexity reduction of explicit MPC based on fuzzy reshaped polyhedrons for use in industrial controllers. International Journal of Systems Science, č. 3, zv. 54, str. 463-477, 2023.
  • Tsai, Ying-Kuan – Malak, Jr., Richard J.: Design of Approximate Explicit Model Predictive Controller Using Parametric Optimization. Journal of Mechanical Design, č. 12, zv. 144, 2022.
  • Galcikova, Lenka – Oravec, Juraj: Fixed complexity solution of partial explicit MPC. Computers & Chemical Engineering, č. 107606, zv. 157, 2022.
  • Provan, Gregory – Sohege, Yves: Robust Embedded Control using Randomized Switching Algorithms. V 2023 European Control Conference, ECC, 2023.
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       28
  • 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.
  • Liu, Qibo – Li, Shaoyuan – Zheng, Yi – Qi, Chenkun – Luo, Min: Learning-Based Distributed Model Predictive Control Approximation Scheme With Guarantees. IEEE Transactions on Industrial Informatics, 2023.
  • Sabir, Zulqurnain – Baleanu, Dumitru – Alhazmi, Sharifah E. – Ben Said, Salem: Heuristic computing with active set method for the nonlinear Rabinovich-Fabrikant model. Heliyon, č. 11, zv. 9, 2023.
  • Emori, E. Y. – Ravagnani, M. A. S. S. – Costa, C. B. B.: An Advanced Control Strategy for the Evaporation Section of An Integrated First- and Second-Generation Ethanol Sugarcane Biorefinery. Chemical and Biochemical Engineering Quarterly, č. 1, zv. 37, str. 17-32, 2023.
  • Leonow, Sebastian – Dyrska, Raphael – Moennigmann, Martin: Embedded Implementation of a Neural Network emulating Nonlinear MPC in a process control application. V 2023 European Control Conference, ECC, 2023.
  • Norouzi, Armin – Heidarifar, Hamed – Borhan, Hoseinali – Shahbakhti, Mahdi – Koch, Charles Robert: Integrating Machine Learning and Model Predictive Control for automotive applications: A review and future directions. Engineering Applications of Artificial Intelligence, č. 105878, zv. 120, 2023.
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       8
  • 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.
  • Teófilo P. G. Mendes – Leizer Schnitman – Idelfonso Bessa dos Reis Nogueira – Ana Mafalda Almeida Peixoto Ribeiro – Alírio Egídio Rodrigues – José Miguel Loureiro – Márcio A.F. Martins: A new Takagi-Sugeno-Kang model-based stabilizing explicit MPC formulation: An experimental case study with implementation embedded in a PLC. Expert Systems with Applications, zv. 210, str. 118369, 2022.
  • Gupta, Nikita – De, Riju – Kodamana, Hariprasad – Bhartiya, Sharad: Batch-to-Batch Adaptive Iterative Learning Control─ Explicit Model Predictive Control Two-Tier Framework for the Control of Batch Transesterification Process. ACS omega, č. 45, zv. 7, str. 41001–41012, 2022.
  • Tijani, Tunde Mufutau – Jimoh, Isah Abdulrasheed: Optimal control of the double inverted pendulum on a cart: A comparative study of explicit MPC and LQR. Applications of Modelling and Simulation, zv. 5, str. 74–87, 2021.
  • Aouaichia, Abdelhadi – Kara, Kamel – Benrabah, Mohamed – Hadjili, Mohamed Laid: Constrained Neural Network Model Predictive Controller Based on Archimedes Optimization Algorithm with Application to Robot Manipulators. Journal of Control, Automation and Electrical Systems, č. 6, zv. 34, str. 1159–1178, 2023.
  • Srishti – Sharma, Sudeep – Padhy, Prabin K: Comparative Study of Inverted Pendulum with Various Types of Controllers. V 2021 International Conference on Control, Automation, Power and Signal Processing (CAPS), str. 1-5, 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       12
  • 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.
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  • 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.
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