A Motion Control Algorithm of Wheel-Legged Robot via Adaptive Dynamic Programming and Data-Driven Value Iteration
Xuefei Liu, Wai Tuck Chow, Youzhi Xu, Yi Sun, Andong Jiang, Ping Zhang, Aihong Ji
Abstract
To enhance the motion adaptability of wheel-legged robot in complex environments, a novel data-driven value iteration (VI) control algorithm based on adaptive dynamic programming (ADP) is proposed. A state-space system model of the wheel-legged robot is established, from which the algebraic Riccati equation containing the weighting matrices is derived based on the Hamilton-Jacobi-Bellman equation. The ADP framework is employed to collect state variable data under external disturbance, and an offline VI-based iteration learning process is conducted to obtain a set of optimal gain matrices. These gain matrices are subsequently integrated with multi-subtask motion control and torque allocation strategy to construct a unified control framework. Finally, a series of experimental evaluations, including dynamic balance, disturbance robustness, and stair-crossing, are conducted. The experimental results demonstrate that the proposed algorithm exhibits strong adaptability and superior disturbance robustness across complex and unstructured terrains.
BibTeX
@inproceedings{ral2026_amotioncontrolal,
title = {A Motion Control Algorithm of Wheel-Legged Robot via Adaptive Dynamic Programming and Data-Driven Value Iteration},
author = {Xuefei Liu and Wai Tuck Chow and Youzhi Xu and Yi Sun and Andong Jiang and Ping Zhang and Aihong Ji},
booktitle = {RA-L 2026},
year = {2026}
}