Position:
Lecturer
Department:
Department of Information Engineering and Process Control (DIEPC)
Room:
NB 652
eMail:
Home page:
https://www.uiam.sk/~kvasnica/
Phone:
+421 259 325 352
Skype:
mkvasnica
ORCID iD:
0000-0001-8699-551X
WoS ResearcherID:
A-7022-2013
Google Scholar:
pE8c4hsAAAAJ
Research activities:
Model Predictive Control, Hybrid Systems, Process Control
Availability:

Citations

  • Total citations       2380

R. KohútL. GalčíkováK. FedorováT. Ábelová – M. Bakošová – M. Kvasnica: Hidden Markov Model-based Warm-start of Active Set Method in Model Predictive Control. Editor(s): R. Paulen and M. Fikar, In Proceedings of the 23rd International Conference on Process Control, IEEE, Slovak University of Technology, 2021.
  • Number of citations       1
  • 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, vol. 120, 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.
Y. Jiang – J. Oravec – B. Houska – M. Kvasnica: Parallel MPC for Linear Systems with Input Constraints. IEEE Transactions on Automatic Control, no. 7, vol. 66, pp. 3401–3408, 2021.
  • Number of citations       7
  • Cao, Wanke – Liu, Shao – Li, Jianwei – Zhang, Zhaolong – He, Hongwen: Analysis and Design of Adaptive Cruise Control for Smart Electric Vehicle With Domain-Based Poly-Service Loop Delay. IEEE Transactions on Industrial Electronics, no. 1, vol. 70, pp. 866-877, 2023.
  • Mendes, Teofilo P. G. – Schnitman, Leizer – Nogueira, Idelfonso Bessa dos Reis – Ribeiro, Ana Mafalda Almeida Peixoto – Rodrigues, Alirio Egidio – Loureiro, Jose Miguel – Martins, Marcio A. F.: 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, no. 118369, vol. 210, 2022.
  • Hu, Zhongrui – Shi, Peng – Wu, Ligang: Polytopic Event-Triggered Robust Model Predictive Control for Constrained Linear Systems. IEEE Transactions on Circuits and Systems I-regular Papers, no. 6, vol. 68, pp. 2594-2603, 2021.
  • Sarkka, Simo – Garcia-Fernandez, Angel F.: Temporal Parallelization of Dynamic Programming and Linear Quadratic Control. IEEE Transactions on Automatic Control, no. 2, vol. 68, pp. 851-866, 2023.
  • Lupu, Daniela – Necoara, Ion: Deep unfolding projected first order methods-based architectures: application to linear model predictive control. In 2023 European Control Conference, ECC, 2023.
  • Jugade, Chaitanya – Ingole, Deepak – Sonawane, Dayaram N. – Kvasnica, Michal – Gustafson, John: A Memory Efficient FPGA Implementation of Offset-Free Explicit Model Predictive Controller. IEEE Transactions on Control Systems Technology, no. 6, vol. 30, pp. 2646-2657, 2022.
  • Houska, Boris – Shi, Jiahe: Distributed MPC with ALADIN-A Tutorial. In 2022 American Control Conference (ACC), pp. 358-363, 2022.
J. Theunissen – A. Sorniotti – P. Gruber – S. Fallah – M. Ricco – M. Kvasnica – M. Dhaens: Regionless Explicit Model Predictive Control of Active Suspension Systems With Preview. IEEE Transactions on Industrial Electronics, no. 6, vol. 67, pp. 4877–4888, 2020.
  • Number of citations       27
  • Ni, T. – Li, W. – Zhao, D. – Kong, Z.: Road profile estimation using a 3D sensor and intelligent vehicle. Sensors (Switzerland), no. 13, vol. 20, pp. 1-17, 2020.
  • Enders, E. – Burkhard, G. – Munzinger, N.: Analysis of the influence of suspension actuator limitations on ride comfort in passenger cars using model predictive control. Actuators, no. 3, vol. 9, 2020.
  • Nie, Z. – Li, Z. – Wang, W. – Zhao, W. – Lian, Y. – Outbib, R.: Gain-scheduling control of dynamic lateral lane change for automated and connected vehicles based on the multipoint preview. IET Intelligent Transport Systems, no. 10, vol. 14, pp. 1338-1349, 2020.
  • Dong, J.-F. – Han, S.-Y. – Zhou, J. – Chen, Y.-H. – Zhong, X.-F.: FNT-Based Road Profile Classification in Vehicle Semi-Active Suspension System. IEEE Transactions on Systems, Man, and Cybernetics: Systems, no. 9283481, vol. 2020-October, pp. 1392-1397, 2020.
  • Huang, S.-D. – Cao, G.-Z. – Xu, J. – Cui, Y. – Wu, C. – He, J.: Predictive Position Control of Long-Stroke Planar Motors for High-Precision Positioning Applications. IEEE Transactions on Industrial Electronics, no. 1, vol. 68, pp. 796-811, 2021.
  • Jose, J.T. – Das, J. – Mishra, S.K.: Dynamic Improvement of Hydraulic Excavator Using Pressure Feedback and Gain Scheduled Model Predictive Control. IEEE Sensors Journal, no. 17, vol. 21, pp. 18526-18534, 2021.
  • Rodriguez-Guevara, D. – Favela-Contreras, A. – Beltran-Carbajal, F. – Sotelo, D. – Sotelo, C.: Active suspension control using an mpc-lqr-lpv controller with attraction sets and quadratic stability conditions. Mathematics, no. 20, vol. 9, 2021.
  • Liang, Y. – Li, Y. – Khajepour, A. – Zheng, L.: Holistic Adaptive Multi-Model Predictive Control for the Path following of 4WID Autonomous Vehicles. IEEE Transactions on Vehicular Technology, no. 1, vol. 70, pp. 69-81, 2021.
  • Wang, R. – Liu, W. – Ding, R. – Meng, X. – Sun, Z. – Yang, L. – Sun, D.: Switching control of semi-active suspension based on road profile estimation. Vehicle System Dynamics, 2021.
  • Gilimalage, A.S.M. – Kimura, S.: Model predictive control-based control algorithm for a target-chaser maneuvering situation. Advanced Robotics, no. 21-22, vol. 35, pp. 1265-1276, 2021.
  • Bélai, I. – Huba, M. – Vrancic, D.: Comparing traditional and constrained disturbance-observer based positional control. Measurement and Control (United Kingdom), no. 3-4, vol. 54, pp. 170-178, 2021.
  • Rokonuzzaman, M. – Mohajer, N. – Nahavandi, S. – Mohamed, S.: Review and performance evaluation of path tracking controllers of autonomous vehicles. IET Intelligent Transport Systems, no. 5, vol. 15, pp. 646-670, 2021.
  • Li, W.-H. – Ni, T. – Zhao, D.-X. – Zhang, P.-H. – Shi, X.-B.: Active suspension control method of high mobility rescue vehicle based on ensemble Kalman filter [基 于 集 合 卡 尔 曼 滤 波 的 高 机 动 救 援 车 辆主 动 悬 挂 控 制 方 法]. Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition), no. 12, vol. 52, pp. 2816-2826, 2022.
  • Chen, X. – Zeng, M. – Liu, X. – Jiang, A.: Research on Semi-active Suspension Preview Control Based on VSL-MPC [基于 VSL-MPC 的半主动悬架预瞄控制研究 *]. Qiche Gongcheng/Automotive Engineering, no. 10, vol. 44, pp. 1537-1546, 2022.
  • Ghamari, S.M. – Khavari, F. – Molaee, H. – Wheeler, P.: Generalised model predictive controller design for A DC–DC non-inverting buck–boost converter optimised with a novel identification technique. IET Power Electronics, no. 13, vol. 15, pp. 1350-1364, 2022.
  • Zhang, M. – Jing, X.: Energy-Saving Robust Saturated Control for Active Suspension Systems via Employing Beneficial Nonlinearity and Disturbance. IEEE Transactions on Cybernetics, no. 10, vol. 52, pp. 10089-10100, 2022.
  • Liu, B. – Zhao, D. – Chang, J. – Yao, S. – Ni, T. – Gong, M.: Statistical terrain model with geometric feature detection based on GPU using LiDAR on vehicles. Measurement Science and Technology, no. 9, vol. 33, 2022.
  • Oravec, J. – Klaučo, M.: Real-time tunable approximated explicit MPC. Automatica, no. 110315, vol. 142, 2022.
  • Li, Z. – Li, Z. – Liu, C.: Vertical vibration of hub motor driven electric vehicle based on EMPC [基于显式模型预测控制的轮毂驱动电动车垂向振动研究]. Zhendong yu Chongji/Journal of Vibration and Shock, no. 11, vol. 41, pp. 259-265, 2022.
  • Deng, Y. – Gong, M. – Ni, T.: Double-channel event-triggered adaptive optimal control of active suspension systems. Nonlinear Dynamics, no. 4, vol. 108, pp. 3435-3448, 2022.
  • Li, Y. – Wang, T. – Liu, W. – Tong, S.: Neural Network Adaptive Output-Feedback Optimal Control for Active Suspension Systems. IEEE Transactions on Systems, Man, and Cybernetics: Systems, no. 6, vol. 52, pp. 4021-4032, 2022.
  • Rodriguez-Guevara, D. – Favela-Contreras, A. – Beltran-Carbajal, F. – Sotelo, C. – Sotelo, D.: An MPC-LQR-LPV Controller with Quadratic Stability Conditions for a Nonlinear Half-Car Active Suspension System with Electro-Hydraulic Actuators. Machines, no. 2, vol. 10, 2022.
  • Chen, Y. – Zhang, Y. – Cai, B.: Suspension Strategy of Maglev Vertical Axis Wind Turbine Based on Sliding Mode Adaptive Neural Network Predictive Control. IEEE Access, vol. 10, pp. 91712-91721, 2022.
  • Li, D. – Liu, F. – Deng, J. – Tang, Z. – Wang, Y.: Nonlinear Damping Curve Control of Semi-Active Suspension Based on Improved Particle Swarm Optimization. IEEE Access, vol. 10, pp. 90958-90970, 2022.
  • Jeong, Y. – Sohn, Y. – Chang, S. – Yim, S.: Design of Virtual Reference Feedforward Controller for an Active Suspension System. IEEE Access, vol. 10, pp. 65671-65684, 2022.
  • Ahmad, S.: Model Predictive Control for the Level Control Loop of the FESTO MPS PA Compact Workstation. In 2022 Advances in Science and Engineering Technology International Conferences, ASET 2022, 2022.
  • Wang, R. – Liu, W. – Ding, R. – Meng, X. – Sun, Z. – Yang, L. – Sun, D.: Switching control of semi-active suspension based on road profile estimation. Vehicle System Dynamics, no. 6, vol. 60, pp. 1972-1992, 2022.
M. Furka – M. 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.
P. Berner – K. König – M. Kvasnica – M. Mönnigmann: Robust Event-triggered Networked MPC with Active Set Updates. Editor(s): M. Fikar and M. Kvasnica, In Proceedings of the 22nd International Conference on Process Control, Slovak Chemical Library, Štrbské Pleso, Slovakia, pp. 7–12, 2019.
  • Number of citations       1
  • He, N. – Qi, L. – Xu, Z. – Du, J.: Event-Driven Model Predictive Controller for State Constrained Systems: An Input Signal Reconstruction Method. IEEE Access, no. 9432820, vol. 9, pp. 74209-74217, 2021.
J. OravecJ. HolazaM. Horváthová – N. A. Nguyen – M. Kvasnica – M. Bakošová: Convex-lifting-based robust control design using the tunable robust invariant sets. European Journal of Control, vol. 49, pp. 44–52, 2019.
  • Number of citations       2
  • 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.
  • Xu, Jie – Sun, Sizhou – Lu, Huacai: Research on torque feedback equivalent structures in the multi-layer and multi-axis synchronisation control model. Iet Circuits Devices & Systems, no. 4, vol. 16, pp. 301-310, 2022.
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.
J. Števek – M. KvasnicaM. Fikar – A. Gomola: A Parametric Programming Approach to Automated Integrated Circuit Design. IEEE Transactions on Control Systems Technology, no. 4, vol. 26, pp. 1180–1191, 2018.
  • Number of citations       1
  • Kouhalvandi, Lida – Ceylan, Osman – Ozoguz, Serdar: Optimization techniques for analog and RF circuit designs: an overview. Analog Integrated Circuits and Signal Processing, no. 3, vol. 106, pp. 511-524, 2021.
J. Drgoňa – D. Picard – M. Kvasnica – L. Helsen: Approximate model predictive building control via machine learning. Applied Energy, vol. 218, pp. 199–216, 2018.
  • Number of citations       112
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