収縮計量を用いた認証付き制御の学習
Learning Certified Control using Contraction Metric
ニューラルネットワークで収縮計量と制御器を同時に学習し、任意の初期状態と有界外乱下で目標軌道への収束や追従誤差の上限を保証する認証付き制御則を構成する手法を提案した。
著者: Dawei Sun, Susmit Jha, Chuchu Fan
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
In this paper, we solve the problem of finding a certified control policy that drives a robot from any given initial state and under any bounded disturbance to the desired reference trajectory, with guarantees on the convergence or bounds on the tracking error. Such a controller is crucial in safe motion planning. We leverage the advanced theory in Control Contraction Metric and design a learning framework based on neural networks to co-synthesize the contraction metric and the controller for control-affine systems. We further provide methods to validate the convergence and bounded error guarantees. We demonstrate the performance of our method using a suite of challenging robotic models, including models with learned dynamics as neural networks. We compare our approach with leading methods using sum-of-squares programming, reinforcement learning, and model predictive control. Results show that our methods indeed can handle a broader class of systems with less tracking error and faster execution speed. Code is available at https://github.com/sundw2014/C3M.
