  We study Model Predictive Control (MPC) and propose a general analysis
pipeline to bound its dynamic regret. The pipeline first requires deriving a
perturbation bound for a finite-time optimal control problem. Then, the
perturbation bound is used to bound the per-step error of MPC, which leads to a
bound on the dynamic regret. Thus, our pipeline reduces the study of MPC to the
well-studied problem of perturbation analysis, enabling the derivation of
regret bounds of MPC under a variety of settings. To demonstrate the power of
our pipeline, we use it to generalize existing regret bounds on MPC in linear
time-varying (LTV) systems to incorporate prediction errors on costs, dynamics,
and disturbances. Further, our pipeline leads to regret bounds on MPC in
systems with nonlinear dynamics and constraints.
