自律自転車制御のための適応型データ駆動方策最適化アプローチ
An Adaptive Data-Enabled Policy Optimization Approach for Autonomous Bicycle Control
フィードバック線形化と適応型データ駆動方策最適化を組み合わせ、自律自転車のバランス制御を高精度化した研究。
著者: Niklas Persson, Feiran Zhao, Mojtaba Kaheni, Florian Dörfler, Alessandro V. Papadopoulos
分類: eess.SY, cs.RO, cs.SY, math.OC
原文アブストラクト
This paper presents a unified control framework that integrates a Feedback Linearization (FL) controller in the inner loop with an adaptive Data-Enabled Policy Optimization (DeePO) controller in the outer loop to balance an autonomous bicycle. While the FL controller stabilizes and partially linearizes the inherently unstable and nonlinear system, its performance is compromised by unmodeled dynamics and time-varying characteristics. To overcome these limitations, the DeePO controller is introduced to enhance adaptability and robustness. The initial control policy of DeePO is obtained from a finite set of offline, persistently exciting input and state data. To improve stability and compensate for system nonlinearities and disturbances, a robustness-promoting regularizer refines the initial policy, while the adaptive section of the DeePO framework is enhanced with a forgetting factor to improve adaptation to time-varying dynamics. The proposed DeePO+FL approach is evaluated through simulations and real-world experiments on an instrumented autonomous bicycle. Results demonstrate its superiority over the FL-only approach, achieving more precise tracking of the reference lean angle and lean rate.
