An Adaptive Data-Enabled Policy Optimization Approach for Autonomous Bicycle Control

--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. T o 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. T o 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. N autonomous bicycle is a bicycle, equipped with electric motors, sensors, algorithms, and control systems that allow the bicycle to navigate and operate without human intervention. Autonomous bicycles are an exciting area of research and development with numerous potential applications that can improve transportation, safety, and efficiency. In bicycle-sharing systems, autonomous bicycles can enhance the user experience by autonomously traveling to a person who has requested one, eliminating the need for individuals to walk toward the bicycle [1]. Additionally, autonomous bicycles can streamline fleet management by enabling bicycles to autonomously navigate to charging stations for recharging. This eliminates the need for operators to manually collect, load, and transport bicycles to charging stations, making the process more efficient. This work was supported by the Knowledge Foundation (KKS) with grant "M alardalen University Automation Research Center (MARC)", n. 20240011. Papadopoulos are with the Division of Intelligent Future Technologies, M alardalen University, 721 23 V aster as, Sweden. F. Zhao and F. D orfler are with the Department of Information Technology and Electrical Engineering, ETH Zurich, 8092 Zurich, Switzerland.