Predictive Control and Optimization | AIChE

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Predictive Control and Optimization

Chair(s)

Mesbah, A., University of California, Berkeley

Co-chair(s)

Durand, H., Wayne State University

The session welcomes contributions that consider the optimal control and estimation problem for industrial processes. Topics of interest include the development of model predictive and moving horizon approaches, the derivation and implementation issues of economic control approaches, and unique challenges of real-time optimization based on dynamic descriptions. Theoretical, algorithmic and applications contributions are encouraged.

Presentations

8:00 AM
(434a) Physics-Based Machine Learning Modeling for Model Predictive Control of Nonlinear Processes
8:15 AM
(434b) Diffusing-Horizon Model Predictive Control
8:30 AM
(434c) Fast and Convergent Nonconvex Constrained Distributed Optimization and Its Application to Distributed MPC
8:45 AM
(434d) An Offset-Free Control Framework for Koopman Lyapunov-Based Model Predictive Control
9:00 AM
(434e) Interacting Dynamical System Control By Multi-Parametric Distributed Model Predictive Control
9:15 AM
(434f) A Back-Off Approach for Simultaneous Design and Nonlinear Model Predictive Control of Dynamic Systems Under Uncertainty
9:30 AM
(434g) Goal-Oriented Model Learning with High-Fidelity Simulations Using MPC-Embedded Bayesian Optimization
9:45 AM
(434h) Tractable Control-Theoretic Constraint Design for Lyapunov-Based Economic Model Predictive Control

Topics 

Process Automation & Control

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Pricing

Individuals

AIChE Pro Members $150.00
AIChE Emeritus Members $105.00
AIChE Graduate Student Members Free
AIChE Undergraduate Student Members Free
AIChE Explorer Members $225.00
Non-Members $225.00

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