  This paper deals with a distributed implementation of minimax adaptive
control algorithm for networked dynamical systems modeled by a finite set of
linear models. To hedge against the uncertainty arising out of finite number of
possible dynamics in each node in the network, it collects only the historical
data of its neighboring nodes to decide its control action along its edges.
This makes our proposed distributed approach scalable. Numerical simulations
demonstrate that once each node has sufficiently estimated the uncertain
parameters, the distributed minimax adaptive controller behaves like the
optimal distributed H-infinity controller in hindsight.
