Position:
Deputy of institute for research
Head of department
Lecturer
Department:
Department of Information Engineering and Process Control (DIEPC)
Room:
NB 663
eMail:
Home page:
https://www.uiam.sk/~klauco
Phone:
+421 259 325 345
Skype:
m.klauco
ORCID iD:
0000-0003-0098-2625
WoS ResearcherID:
G-3973-2015
Google Scholar:
wVXDzr8AAAAJ
Availability:

Citations

  • Total citations       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, vol. 116, pp. 80–92, 2022.
  • Number of citations       3
  • Cai, Panpan – Hsu, David: Closing the Planning-Learning Loop With Application to Autonomous Driving. IEEE Transactions on Robotics, no. 2, vol. 39, pp. 998-1011, 2023.
  • Schwung, Andreas – Yuwono, Steve: Model Predictive Control with Adaptive PLC-based Policy on Low Dimensional State Representation for Industrial Applications. In 2023 31st Mediterranean Conference on Control and Automation, Med, pp. 883-889, 2023.
  • Walter, Daniel – Vasquez-Varas, Donato – Kunisch, Karl: Learning Optimal Feedback Operators and their Sparse Polynomial Approximations. Journal of Machine Learning Research, no. 301, vol. 24, 2023.
J. OravecM. Klaučo: Real-time tunable approximated explicit MPC. Automatica, vol. 142, pp. 110315, 2022.
  • Number of citations       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, no. 7, vol. 13, 2023.
K. KišM. KlaučoM. Kvasnica: Explicit MPC in the form of Sparse Neural Networks. Editor(s): R. Paulen and M. Fikar, In Proceedings of the 23rd International Conference on Process Control, IEEE, Slovak University of Technology, pp. 163–168, 2021.
  • Number of citations       2
  • Leonow, Sebastian – Dyrska, Raphael – Moennigmann, Martin: Embedded Implementation of a Neural Network emulating Nonlinear MPC in a process control application. In 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, no. 16, vol. 55, pp. 142-147, 2022.
D. Efremov – T. Haniš – M. Klaučo: Haptic Driver Guidance for Lateral Driving Envelope Protection Using Model Predictive Control. In IEEE Intelligent Vehicles Symposium, IEEE Xplore, Las Vegas, NV, USA, USA, 2021.
  • Number of citations       1
  • Noubissie Tientcheu, Simplice Igor – Du, Shengzhi – Djouani, Karim: Review on Haptic Assistive Driving Systems Based on Drivers\\\' Steering-Wheel Operating Behaviour. Electronics, no. 13, vol. 11, 2022.
Y. Lohr – M. KlaučoM. Fikar – M. Mönnigmann: Machine Learning Assisted Solutions of Mixed Integer MPC on Embedded Platforms. Editor(s): Rolf Findeisen, Sandra Hirche, Klaus Janschek, Martin Mönnigmann, In Preprints of the 21st IFAC World Congress (Virtual), Berlin, Germany, July 12-17, 2020, vol. 21, 2020.
  • Number of citations       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, no. 110690, vol. 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, no. 6, vol. 44, pp. 3139-3167, 2023.
  • Decardi-Nelson, B. – You, F.: Optimal energy management in greenhouses using distributed hybrid DRL-MPC framework. Computer Aided Chemical Engineering, vol. 52, pp. 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. In Proceedings of Machine Learning Research, pp. 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(s): Rolf Findeisen, Sandra Hirche, Klaus Janschek, Martin Mönnigmann, In Preprints of the 21st IFAC World Congress (Virtual), Berlin, Germany, July 12-17, 2020, vol. 21, 2020.
  • Number of citations       3
  • Thormann, Sebastian – Schirrer, Alexander – Jakubek, Stefan: Safe and Efficient Cooperative Platooning. IEEE Transactions on Intelligent Transportation Systems, no. 2, vol. 23, pp. 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, no. 3, vol. 71, pp. 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, no. 12, SI, vol. 16, pp. 1710-1725, 2022.
D. Efremov – M. Klaučo – T. Haniš – M. Hromčík: Driving Envelope Definition and Envelope Protection Using Model Predictive Control. In Proceedings of the American Control Conference, Denver, Colorado, USA, 2020.
  • Number of citations       3
  • Noubissie Tientcheu, Simplice Igor – Du, Shengzhi – Djouani, Karim: Review on Haptic Assistive Driving Systems Based on Drivers\\\' Steering-Wheel Operating Behaviour. Electronics, no. 13, vol. 11, 2022.
  • Gao, Liming – Beal, Craig – Fescenmyer, Daniel – Brennan, Sean: Analytical Longitudinal Speed Planning for CAVs with Previewed Road Geometry and Friction Constraints. In 2021 IEEE Intelligent Transportation Systems Conference (itsc), pp. 1610-1615, 2021.
  • Efremov, Denis – Zhyliaiev, Yehor – Kashel, Bohdan – Hanis, Tomas: Lateral Driving Envelope Protection Using Cascade Control. In 2021 21st International Conference on Control, Automation and Systems (iccas 2021), pp. 1440-1446, 2021.
K. KišM. Klaučo: Neural network based explicit MPC for chemical reactor control. Acta Chimica Slovaca, no. 2, vol. 12, pp. 218–223, 2019.
  • Number of citations       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, no. 124124, vol. 277, 2020.
  • Tsai, Ying-Kuan – Malak, Jr., Richard J.: Design of Approximate Explicit Model Predictive Controller Using Parametric Optimization. Journal of Mechanical Design, no. 12, vol. 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, no. 7, vol. 11, 2023.
  • Furka, Matus – Kis, Karol – Horvathova, Michaela – Mojto, Martin – Bakosova, Monika: Identification and Control of a Cascade of Biochemical Reactors. In 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, no. 101561, vol. 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, vol. 183, pp. 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, no. 3, vol. 24, pp. 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, no. 124124, vol. 277, 2020.
  • Dutta, Debaprasad – Upreti, Simant R.: Artificial intelligence-based process control in chemical, biochemical, and biomedical engineering. Canadian Journal of Chemical Engineering, no. 11, vol. 99, pp. 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, no. 127905, vol. 413, 2021.
M. FurkaM. KlaučoM. Kvasnica: Stabilization of Furuta Pendulum using Nonlinear MPC. Research Papers Faculty of Materials Science and Technology in Trnava, no. 45, vol. 27, pp. 42–48, 2019.
  • Number of citations       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, no. 3, vol. 33, pp. 1–12, 2022.
  • Homburger, Hannes – Wirtensohn, Stefan – Reuter, Johannes: Swinging up and stabilization control of the Furuta pendulum using model predictive path integral control. In 2022 30th Mediterranean Conference on Control and Automation (MED), pp. 7–12, 2022.
M. KalúzM. KlaučoĽ. ČirkaM. Fikar: Flexy2: A Portable Laboratory Device for Control Engineering Education. In 12th IFAC Symposium Advances in Control Education, pp. 159–164, 2019.
  • Number of citations       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, vol. 8, pp. 170183-170194, 2020.
  • J. L. Villa – S. Sanchez: Implementing a Software-based Controller as a Strategy for Teaching Digital Control. In 2020 IX International Congress of Mechatronics Engineering and Automation (CIIMA), pp. 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, no. 1, SI, vol. 29, pp. 287-305, 2021.
  • Dusek, F. – Honc, D. – Mrazek, M.: RCDue - Laboratory System for Teaching Automation and Control - Concept of the system. In Proceedings of the 2021 23rd International Conference on Process Control, PC 2021, pp. 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, no. 6, vol. 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, no. 17, vol. 55, pp. 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, no. 2, vol. 11, 2022.
  • Cardoso, Alberto – Oliveira, Paulo Moura – Sa, Joao: Pocket Labs as a STEM Learning Tool and for Engineering Motivation. In Learning in the Age of Digital and Green Transition, Icl2022, Vol 1, pp. 413-422, 2023.
Y. Lohr – M. KlaučoM. Kalúz – M. Mönnigmann: Mimicking Predictive Control with Neural Networks in Domestic Heating Systems. Editor(s): M. Fikar and M. Kvasnica, In Proceedings of the 22nd International Conference on Process Control, Slovak Chemical Library, Štrbské Pleso, Slovakia, pp. 19–24, 2019.
  • Number of citations       1
  • M. Furka – K. Kiš – M. Horváthová – M. Mojto – M. Bakošová: Identification and Control of a Cascade of Biochemical Reactors. In 2020 Cybernetics Informatics (K I), pp. 1-6, 2020.
M. KvasnicaP. BakaráčM. Klaučo: Complexity reduction in explicit MPC: A reachability approach. Systems & Control Letters, vol. 124, pp. 19–26, 2019.
  • Number of citations       14
  • Mönnigmann, M. – Pannocchia, G.: Reducing the computational effort of MPC with closed-loop optimal sequences of affine laws. In IFAC-PapersOnLine, pp. 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. In 2022 American Control Conference (ACC), pp. 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, no. 2, vol. 8, pp. 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, no. 2, vol. 53, pp. 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, no. 2, vol. 53, pp. 11362-11367, 2020.
  • Jugade, Chaitanya – Ingole, Deepak – Sonawane, Dayaram – Kvasnica, Michal – Gustafson, John: Memory-Efficient Explicit Model Predictive Control using Posits. In 2019 Sixth Indian Control Conference (icc), pp. 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, no. 1, vol. 8, pp. 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, no. 1, vol. 11, pp. 338-353, 2023.
  • Belai, Igor – Huba, Mikulas – Vrancic, Damir: Comparing traditional and constrained disturbance-observer based positional control. Measurement & Control, no. 3-4, vol. 54, pp. 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, no. 109429, vol. 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, no. 3, vol. 54, pp. 463-477, 2023.
  • Tsai, Ying-Kuan – Malak, Jr., Richard J.: Design of Approximate Explicit Model Predictive Controller Using Parametric Optimization. Journal of Mechanical Design, no. 12, vol. 144, 2022.
  • Galcikova, Lenka – Oravec, Juraj: Fixed complexity solution of partial explicit MPC. Computers & Chemical Engineering, no. 107606, vol. 157, 2022.
  • Provan, Gregory – Sohege, Yves: Robust Embedded Control using Randomized Switching Algorithms. In 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, vol. 77, pp. 1–8, 2019.
  • Number of citations       28
  • Masti, Daniele – Bemporad, Alberto: Learning binary warm starts for multiparametric mixed-integer quadratic programming. In 2019 18th European Control Conference (ECC), pp. 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. In IFAC-PapersOnLine, pp. 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, no. 109247, vol. 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, no. 4, vol. 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, no. NM0HO, vol. 92, pp. 261-270, 2020.
  • Bertsimas, Dimitris – Stellato, Bartolomeo: The voice of optimization. Machine Learning, no. 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, no. 6, vol. 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. In 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, vol. 97, pp. 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, no. 103585, vol. 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), vol. 1088, pp. 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, vol. 9, pp. 132897-132913, 2021.
  • Bertsimas, D. – Stellato, B.: The voice of optimization. Machine Learning, no. 2, vol. 110, pp. 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, no. 2, vol. 136, 2021.
  • Stomberg, G. – Engelmann, A. – Faulwasser, T.: A distributed active set method for model predictive control. In IFAC-PapersOnLine, pp. 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, no. 106922, vol. 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), no. 18, vol. 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, no. 10, vol. 70, pp. 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, no. 19, vol. 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, no. 4, vol. 106, pp. 3199-3214, 2021.
  • Ławryńczuk, M.: Introduction to Model Predictive Control. Studies in Systems, Decision and Control, vol. 389, pp. 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, no. 109947, vol. 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, no. 1, vol. 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, no. 11, vol. 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, no. 1, vol. 37, pp. 17-32, 2023.
  • Leonow, Sebastian – Dyrska, Raphael – Moennigmann, Martin: Embedded Implementation of a Neural Network emulating Nonlinear MPC in a process control application. In 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, no. 105878, vol. 120, 2023.
P. BakaráčJ. HolazaM. KlaučoM. Kalúz – J. Löfberg – M. Kvasnica: Explicit MPC based on Approximate Dynamic Programming. In European Control Conference 2018, Limassol, Cyprus, pp. 1172–1177, 2018.
  • Number of citations       8
  • Moennigmann, Martin: On the structure of the set of active sets in constrained linear quadratic regulation. Automatica, vol. 106, pp. 61-69, 2019.
  • Gulan, M. – Minarcik, P. – Kulhanek, J.: Energy-efficient Swing-up and MPC Stabilization of an Inverted Pendulum. In Proceedings of the 2019 22nd International Conference on Process Control, PC 2019, pp. 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, vol. 210, pp. 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, no. 45, vol. 7, pp. 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, vol. 5, pp. 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, no. 6, vol. 34, pp. 1159–1178, 2023.
  • Srishti – Sharma, Sudeep – Padhy, Prabin K: Comparative Study of Inverted Pendulum with Various Types of Controllers. In 2021 International Conference on Control, Automation, Power and Signal Processing (CAPS), pp. 1-5, 2021.
P. BakaráčM. KlaučoM. Fikar: Comparison of Inverted Pendulum Stabilization with PID, LQ, and MPC Control. Editor(s): J. Cigánek, Š. Kozák, A. Kozáková, In 2018 Cybernetics & Informatics (K&I), Slovak Chemical Library, Bratislava, Lazy pod Makytou, Slovakia, vol. 29, 2018.
  • Number of citations       12
  • A. Barkat – A. Hanif – M. T. Hamayun: Model Identification and Control of a Lab Based Inverted Pendulum System Using Robust Control Technique. In 2018 International Conference on Frontiers of Information Technology (FIT), pp. 1-6, 2018.
  • Manai, N.E. – Saidi, I. – Soudani, D.: Predictive control of an under-actuated System. In Proceedings of International Conference on Advanced Systems and Emergent Technologies, IC_ASET 2019, pp. 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, no. 4, vol. 9, pp. 1-26, 2020.
  • Hidayati, A.N. – Wasiwitono, U.: Modeling and Control of Inertia Wheel Pendulum System with LQR and PID control. In Proceedings - 2021 International Seminar on Intelligent Technology and Its Application: Intelligent Systems for the New Normal Era, ISITIA 2021, pp. 135-140, 2021.
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