- Position:
- Lecturer
Researcher
PhD student - Department:
- Department of Information Engineering and Process Control (DIEPC)
- Room:
- NB 633
- eMail:
- Home page:
- https://www.uiam.sk/~kis
- Phone:
- +421 259 325 176
- ORCID iD:
- 0000-0002-8294-9871
- WoS ResearcherID:
- ABD-4372-2020
- Google Scholar:
- MgqCvIoAAAAJ
- Availability:
Citations
- Total citations 15
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.
K. Kiš – M. Klaučo – M. 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.
K. Kiš – M. 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.
