About Fahad Albalawi Fahad Albalawi Research Scientist, Electrical and Computer Engineering Controller design Model Predictive Signal processing Fahad Albalawi is a research scientist in the Electrical Engineering department within the CEMSE division. He got an M.Sc. in Electrical Engineering from George Washington University, the United States where he worked in Process control theory, Robotics and Applied mathematics. He obtained his Ph.D. degree in Electrical Engineering from the University of California Los Angeles ( UCLA) where he worked in optimization, model predictive control, process operational safety, and system identification. He joined professor Meriem group at King Abdullah University of Science and Technology (KAUST) Events Presented Events Apr 12 - Apr 18, 2020 Economic Model Predictive Control Strategies for Nonlinear Systems Fahad Albalawi , Research Scientist, Electrical and Computer Engineering Apr 16, 16:00 - 17:30 KAUST Model Predictive Control neural network nonlinear control systems The first part of this talk provides a brief introduction of the EMPC (motivation, challenges, and solutions). The second part of this talk proposes a regret-based robust EMPC paradigm for nonlinear systems subject to unknown but bounded disturbance. The main motivation of the proposed work is the possible improvement of the economic performance when one considers the regret function as the objective function for the robust EMPC algorithm instead of the worst cost. The third part of this talk introduces an integrated framework that combines a Neural Network (NN) algorithm with an MPC scheme that can guarantee closed-loop stability in the presence of deception cyberattacks (e.g., min-max cyberattack). Both discrete-time and continuous-time nonlinear systems will be utilized throughout the talk to demonstrate the applicability and effectiveness of the proposed control methods. Finally, future research directions will be presented at the end of the talk. Apr 28 - May 4, 2019 Model Predictive Control Systems for Industrial Applications Fahad Albalawi , Research Scientist, Electrical and Computer Engineering Apr 28, 09:30 - 10:30 B3 L5 R5209 Controller design Model Predictive Control Signal processing Model Predictive Control (MPC) is an d advanced control strategy widely used in the process industries and beyond. Therefore, industry is interested in the developments of MPC formulations that can enhance safety, reliability, and economic profitability of chemical processes. Motivated by these considerations, the first part of this talk focuses on the development of methods for integrating process operational safety and process economics within model predictive control system designs.
Economic Model Predictive Control Strategies for Nonlinear Systems Fahad Albalawi , Research Scientist, Electrical and Computer Engineering Apr 16, 16:00 - 17:30 KAUST Model Predictive Control neural network nonlinear control systems The first part of this talk provides a brief introduction of the EMPC (motivation, challenges, and solutions). The second part of this talk proposes a regret-based robust EMPC paradigm for nonlinear systems subject to unknown but bounded disturbance. The main motivation of the proposed work is the possible improvement of the economic performance when one considers the regret function as the objective function for the robust EMPC algorithm instead of the worst cost. The third part of this talk introduces an integrated framework that combines a Neural Network (NN) algorithm with an MPC scheme that can guarantee closed-loop stability in the presence of deception cyberattacks (e.g., min-max cyberattack). Both discrete-time and continuous-time nonlinear systems will be utilized throughout the talk to demonstrate the applicability and effectiveness of the proposed control methods. Finally, future research directions will be presented at the end of the talk.
Model Predictive Control Systems for Industrial Applications Fahad Albalawi , Research Scientist, Electrical and Computer Engineering Apr 28, 09:30 - 10:30 B3 L5 R5209 Controller design Model Predictive Control Signal processing Model Predictive Control (MPC) is an d advanced control strategy widely used in the process industries and beyond. Therefore, industry is interested in the developments of MPC formulations that can enhance safety, reliability, and economic profitability of chemical processes. Motivated by these considerations, the first part of this talk focuses on the development of methods for integrating process operational safety and process economics within model predictive control system designs.
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