Uncertainty quantification in control problems for flocking models
Mathematical problems in Engineering, 2015•Wiley Online Library
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 steering of the system towards
the desired state even in unstable regimes.
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 steering of the system towards
the desired state even in unstable regimes.
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 steering of the system towards the desired state even in unstable regimes.
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