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Nonlinear Programming Properties and Formulations for Nonlinear and Economic Model Predictive Control

Nonlinear Model Predictive Control (NMPC) is a powerful approach for on-line optimization of nonlinear, dynamic systems. This talk explores nonlinear programming (NLPs) formulations that ensure the stability and robustness of NMPC strategies. In particular, NLPs that satisfy familiar second order and regularity conditions have solutions that are continuous and differentiable with respect to perturbations of the problem data, and are important prerequisites for nominal and ISS stability of NMPC controllers. Moreover, ensuring these properties is not difficult through reformulation of the NLP sub-problem and addition of exact penalty and barrier terms. This framework is also essential for the extension of NMPC to sensitivity-based NMPC and economic NMPC, thus leading to efficient, on-line optimization strategies that guarantee stable and robust performance. The results of this analysis have been implemented within an optimization modeling platform and are demonstrated on challenging case studies drawn from chemical processes.

Type of Seminar:
IfA Seminar
Prof. Lorenz Biegler
Carnegie Mellon University
Jun 06, 2017   14:00 h

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Biographical Sketch:
Lorenz T. (Larry) Biegler is currently the Head and Bayer University Professor of Chemical Engineering at Carnegie Mellon University. His research interests lie in computer aided process engineering (CAPE) and include flowsheet optimization, optimization of systems of differential and algebraic equations, reactor network synthesis and algorithms for constrained, nonlinear process control. Projects in these areas include analysis and development of nonlinear programming algorithms, optimization software design and application to real-world chemical processes and energy systems. He has authored or co-authored over 400 archival publications and two textbooks, edited nine technical books and given numerous invited presentations at national and international conferences.