model predictive control

Friday, March 18, 2016 - 10:30am - 11:00am
Lars Gruene (University of Bayreuth)
Receding Horizon Control (also known as Model Predictive Control) in the sense of this talk is a method for obtaining a feedback-like approximately optimal control for an infinite horizon optimal control problem by iteratively solving a series of finite horizon problems. It can thus be seen as a model reduction method in time. The talk presents conditions under which rigorous statements on the infinite horizon performance of the resulting closed loop trajectory can be made. Stability issues of the closed loop are also briefly addressed.
Monday, February 22, 2016 - 1:15pm - 2:00pm
Stephen Wright (University of Wisconsin, Madison)
We survey some developments in machine learning and data analysis,
focusing on those in which optimization is an important
component. Some of these have possible relevance for industrial and
energy applications, for example, constraints and covariances could be
learned from process data rather than specified a priori. Some
possibilities along these lines will be proposed.
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