Recent Articles and Multimedia
Paper Title: Cautious Optimization via Data Informativity
Authors: Jaap Eising and Jorge Cortés
This paper deals with the problem of accurately determining guaranteed suboptimal values of an unknown cost function on the basis of noisy measurements. The authors consider a set-valued variant to regression where, instead of finding a best estimate of the cost function, they reason over all functions compatible with the measurements and apply robust methods explicitly in terms of the data. The authors showcase the versatility of the proposed methods in two control-relevant problems: data-driven contraction analysis of unknown nonlinear systems and suboptimal regulation with unknown dynamics and cost. Simulations illustrate the results.
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Possible values of an a priori unknown cost function, for regulated trajectories of an a priori unknown dynamical system. The methods of the paper provide guarantees on this behavior on the basis of data.