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

This brief 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 (PE) 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 FL-DeePO approach is evaluated through simulations and real-world experiments on an instrumented autonomous bicycle. Results demonstrate its superiority over the FL-only approach and a reinforcement learning (RL) controller, achieving more precise tracking of the reference lean angle and lean rate.