1分のデータで不安定系を制御：予測制御におけるガウス過程の微分可能性の活用
Learning to Control an Unstable System with One Minute of Data: Leveraging Gaussian Process Differentiation in Predictive Control
ガウス過程の微分可能性を利用して状態依存の線形化モデルを作り、モデル予測制御と組み合わせることで、1分のデータだけでセグウェイのような不安定系を頑健に制御できることを示した論文。
著者: Ivan D. Jimenez Rodriguez, Ugo Rosolia, Aaron D. Ames, Yisong Yue
分類: cs.RO, cs.LG, cs.SY, eess.SY
原文アブストラクト
We present a straightforward and efficient way to control unstable robotic systems using an estimated dynamics model. Specifically, we show how to exploit the differentiability of Gaussian Processes to create a state-dependent linearized approximation of the true continuous dynamics that can be integrated with model predictive control. Our approach is compatible with most Gaussian process approaches for system identification, and can learn an accurate model using modest amounts of training data. We validate our approach by learning the dynamics of an unstable system such as a segway with a 7-D state space and 2-D input space (using only one minute of data), and we show that the resulting controller is robust to unmodelled dynamics and disturbances, while state-of-the-art control methods based on nominal models can fail under small perturbations. Code is open sourced at https://github.com/learning-and-control/core .
