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Poster
Bounded-Regret MPC via Perturbation Analysis: Prediction Error, Constraints, and Nonlinearity
Yiheng Lin · Yang Hu · Guannan Qu · Tongxin Li · Adam Wierman

Tue Nov 29 09:00 AM -- 11:00 AM (PST) @ Hall J #304
in Poster Session 1 »

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.

Keywords: regret · prediction error · Perturbation Analysis · Model Predictive Control

Author Information

Yiheng Lin (California Institute of Technology)
Yang Hu (SEAS, Harvard University)
Guannan Qu (Carnegie Mellon University)
Tongxin Li (Caltech)
Adam Wierman (Caltech)

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