Wang Zhaoyang, Xu Dongfang, Zhao Shunyi, Yu Zehuan, Huang Yan, Ruan Lecheng, Zhou Zhihao, Wang Qining
Department of Advanced Manufacturing and Robotics, College of Engineering, Peking University, Beijing 100871, China.
School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China.
Cyborg Bionic Syst. 2025 Apr 22;6:0248. doi: 10.34133/cbsystems.0248. eCollection 2025.
Hip exoskeleton can provide assistance to users to augment movements in different scenarios. The assistive control for hip exoskeleton involves the interactions among exoskeleton, user, and environment, which depends on the environment perception (to predict locomotion) to design control strategy combined with gait mode and so on. Current exoskeleton control still needs to be improved in adaptation to continuous locomotion mode and different users. To address this problem, we have employed a learning-free (i.e., non-data-driven) environment perception method to improve hip exoskeleton adaptive control toward continuous locomotion mode. The adaptive control experiments were conducted on level ground and stairs on 7 subjects. The prediction accuracy for steady locomotion mode was more than 95% for each subject (ranged from 95.7% to 99.7%). The prediction accuracy for each locomotion mode transition ranged from 87.5% to 100%, and the transition timing could be detected before the end of transition period. Compared with learning-based (data-driven) approaches, our method achieves better performances in adaptive control for hip exoskeleton and shows some generalization for subjects.
髋关节外骨骼可以为使用者提供助力，以增强其在不同场景下的运动能力。髋关节外骨骼的辅助控制涉及外骨骼、使用者和环境之间的相互作用，这依赖于环境感知（以预测运动）来结合步态模式等设计控制策略。当前的外骨骼控制在适应连续运动模式和不同使用者方面仍需改进。为了解决这个问题，我们采用了一种无需学习（即非数据驱动）的环境感知方法来改进髋关节外骨骼对连续运动模式的自适应控制。在7名受试者身上，在平地和楼梯上进行了自适应控制实验。每个受试者在稳定运动模式下的预测准确率均超过95%（范围从95.7%到99.7%）。每种运动模式转换的预测准确率范围为87.5%至100%，并且可以在转换期结束前检测到转换时刻。与基于学习（数据驱动）的方法相比，我们的方法在髋关节外骨骼的自适应控制方面取得了更好的性能，并且对受试者具有一定的通用性。
