Model Predictive Control

Model Predictive Control and Imitation Learning Algorithms for Robot Motion Planning in Physical Human-Robot Interaction

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B9 R2325

This seminar presents a framework for safe and efficient human-robot workspace sharing by using Deep Neural Networks (DNN) and safety filters to rapidly imitate computationally heavy Nonlinear Model Predictive Control method (NMPC), with successful experimental validation on a UR5 manipulator.

Aigerim Nurbayeva

Postdoctoral Research Fellow, Electrical and Computer Engineering

Model Predictive Control Systems for Industrial Applications

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B3 L5 R5209

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

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KAUST

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.

Xingang Guo

Xingang is an MS /Ph . D . student in Electrical Engineering department within CEMSE division at King Abdullah University of Science and Technology. He also hold s a B.Sc. from Northeastern University. His research interests are in the area of estimation and optimal control of nonlinear differential algebraic equations and nonlinear dynamical systems with applications in both membrane distillation desalination and wastewater treatment plants. My research activities are mainly around optimal controller design. For example, a fractional order PID (FOPID) controller was designed for a laser beam