Details

Autor(en) / Beteiligte

• Coulson, Jeremy
• Lygeros, John
• Dorfler, Florian

Titel

Distributionally Robust Chance Constrained Data-Enabled Predictive Control

Ist Teil von

• IEEE transactions on automatic control, 2022-07, Vol.67 (7), p.3289-3304

Ort / Verlag

New York: IEEE

Erscheinungsjahr

2022

Link zum Volltext

• IEEE_Xplore

Quelle

IEEE Electronic Library (IEL)【キャンパス外アクセス可】

Beschreibungen/Notizen

• In this article we study the problem of finite-time constrained optimal control of unknown stochastic linear time-invariant (LTI) systems, which is the key ingredient of a predictive control algorithm-albeit typically having access to a model. We propose a novel distributionally robust data-enabled predictive control (DeePC) algorithm which uses noise-corrupted input/output data to predict future trajectories and compute optimal control inputs while satisfying output chance constraints. The algorithm is based on 1) a nonparametric representation of the subspace spanning the system behavior, where past trajectories are sorted in Page or Hankel matrices; and 2) a distributionally robust optimization formulation which gives rise to strong probabilistic performance guarantees. We show that for certain objective functions, DeePC exhibits strong out-of-sample performance, and at the same time respects constraints with high probability. The algorithm provides an end-to-end approach to control design for unknown stochastic LTI systems. We illustrate the closed-loop performance of the DeePC in an aerial robotics case study.