Uncertainty Quantification in Control Problems for Flocking Models

web_imageGiacomo Albi,  Lorenzo Pareschi, Mattia Zanella.
(03/03/2015  arXiv:1503.00548)

In this paper the optimal control of flocking models with random inputs is investigated from a numerical point of view. The effect of uncertainty in the interaction parameters is studied for a Cucker-Smale type model using a generalized polynomial chaos (gPC) approach. Numerical evidence of threshold effects in the alignment dynamic due to the random parameters is given. The use of a selective model predictive control permits to steer the system towards the desired state even in unstable regimes.

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