  Designing control inputs for a system that involves dynamical responses in
multiple timescales is nontrivial. This paper proposes a parameterized
time-warping function to enable a non-uniformly sampling along a prediction
horizon given some parameters. The horizon should capture the responses under
faster dynamics in the near future and preview the impact from slower dynamics
in the distant future. Then a variable sampling MPC (VS-MPC) strategy is
proposed to jointly determine optimal control and sampling parameters at each
timestamp. VS-MPC adapts how it samples along the horizon and determines
optimal control accordingly at each timestamp without offline tuning or trial
and error. A numerical example of a wind farm battery energy storage system is
also provided to demonstrate that VS-MPC outperforms the uniform sampling MPC.
