AUTHOR=Zhang Jingfan , Li Zhaoxiang , Wang Shuai , Dai Yuan , Zhang Ruirui , Lai Jie , Zhang Dongsheng , Chen Ke , Hu Jie , Gao Weinan , Tang Jianshi , Zheng Yu TITLE=Adaptive optimal output regulation for wheel-legged robot Ollie: A data-driven approach JOURNAL=Frontiers in Neurorobotics VOLUME=16 YEAR=2023 URL=https://www.frontiersin.org/journals/neurorobotics/articles/10.3389/fnbot.2022.1102259 DOI=10.3389/fnbot.2022.1102259 ISSN=1662-5218 ABSTRACT=

The dynamics of a robot may vary during operation due to both internal and external factors, such as non-ideal motor characteristics and unmodeled loads, which would lead to control performance deterioration and even instability. In this paper, the adaptive optimal output regulation (AOOR)-based controller is designed for the wheel-legged robot Ollie to deal with the possible model uncertainties and disturbances in a data-driven approach. We test the AOOR-based controller by forcing the robot to stand still, which is a conventional index to judge the balance controller for two-wheel robots. By online training with small data, the resultant AOOR achieves the optimality of the control performance and stabilizes the robot within a small displacement in rich experiments with different working conditions. Finally, the robot further balances a rolling cylindrical bottle on its top with the balance control using the AOOR, but it fails with the initial controller. Experimental results demonstrate that the AOOR-based controller shows the effectiveness and high robustness with model uncertainties and external disturbances.