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Awards and Recognition | Mathematics and Computer Science Division

Shin Wins Young Author Award at model predictive control conference

Sungho Shin, a postdoctoral appointee in the Mathematics and Computer Science (MCS) Division at Argonne National Laboratory, has won a Young Author Award at the International Federation of Automatic Control – Nonlinear Model Predictive Control Conference.

The prize, created in 1986, is awarded for the best paper of which the first and presenting author is younger than 30 years. The prize includes a certificate and a monetary award.

The paper focuses on a property of dynamic optimization problems known as exponential decay of sensitivity, or EDS, and shows that uniform controllability and observability provide sufficient conditions for EDS. Results with numerical examples provide insights into how perturbations propagate along the time horizon and enable the development of approximation and solution schemes. 

Shin received his Ph.D. degree in chemical engineering from the University of Wisconsin – Madison and joined Argonne as a postdoctoral appointee in May 2021. The award-winning paper was coauthored with Shin’s Ph.D. advisor, Victor Zavala, Baldovin-DaPra Associate Professor at the UW-Madison and a computational scientist in Argonne’s MCS Division.

A special feature of our work is that our proofs are compact and general,” said Shin. This general setting allows us to establish EDS for discrete-time, nonlinear model predictive control and moving horizon estimation problems.”

Shin’s research interests include model predictive control, optimization algorithms, and their applications to large-scale energy infrastructures such as natural gas and power networks. His research with Zavala was funded under the Argonne-led MACSER (Multifaceted Mathematics for Rare, High Impact Events in Complex Energy and Environmental Systems) project.

For the full paper, see Sungho Shin and Victor Zavala, Controllability and Observability Imply Exponential Decay of Sensitivity in Dynamic Optimization, Preprint arx​iv​.org/​a​b​s​/​2​1​0​1​.​06350, 2021.