  We propose a method to attack controllers that rely on external timeseries
forecasts as task parameters. An adversary can manipulate the costs, states,
and actions of the controllers by forging the timeseries, in this case
perturbing the real timeseries. Since the controllers often encode safety
requirements or energy limits in their costs and constraints, we refer to such
manipulation as an adversarial attack. We show that different attacks on
model-based controllers can increase control costs, activate constraints, or
even make the control optimization problem infeasible. We use the linear
quadratic regulator and convex model predictive controllers as examples of how
adversarial attacks succeed and demonstrate the impact of adversarial attacks
on a battery storage control task for power grid operators. As a result, our
method increases control cost by $8500\%$ and energy constraints by $13\%$ on
real electricity demand timeseries.
