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.
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.
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.