クープマン作用素に基づくデータ駆動型移動ロボットシステムのロバスト性向上
Enhancement for Robustness of Koopman Operator-based Data-driven Mobile Robotic Systems
雑音を含む計測下でのクープマン作用素近似の予測誤差を定量化し、データ駆動型ロボット制御にロバスト性を与える手法を提案。一部をオフライン化してリアルタイム実装を可能にし、Gazeboで検証した。
著者: Lu Shi, Konstantinos Karydis
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
Koopman operator theory has served as the basis to extract dynamics for nonlinear system modeling and control across settings, including non-holonomic mobile robot control. There is a growing interest in research to derive robustness (and/or safety) guarantees for systems the dynamics of which are extracted via the Koopman operator. In this paper, we propose a way to quantify the prediction error because of noisy measurements when the Koopman operator is approximated via Extended Dynamic Mode Decomposition. We further develop an enhanced robot control strategy to endow robustness to a class of data-driven (robotic) systems that rely on Koopman operator theory, and we show how part of the strategy can happen offline in an effort to make our algorithm capable of real-time implementation. We perform a parametric study to evaluate the (theoretical) performance of the algorithm using a Van der Pol oscillator and conduct a series of simulated experiments in Gazebo using a non-holonomic wheeled robot.
