华侨大学学报（自然科学版）2026，Vol.47Issue(1)：93-103,11.DOI:10.11830/ISSN.1000-5013.202509064
融合模糊Q学习的脑控机器人共享控制策略
Shared Control Strategy for Brain-Controlled Robotics Integrating Fuzzy Q-Learning
摘要
Abstract
To address the issue that existing fuzzy logic-based shared control methods overly rely on expert experience,a shared control strategy for brain-controlled robotics integrating fuzzy Q-learning is proposed.The method combines fuzzy logic with reinforcement learning,enabling adaptive optimization of human-ma-chine control weights.By designing a reward-penalty function that feeds real-time environmental information back into the system,the human-machine weights are dynamically optimized based on human brain fatigue level and environmental complexity information.The resulting weights are used as coefficients for synthesizing the direction vectors.Comparative experiments against traditional fuzzy logic-based methods demonstrate that the proposed approach enables real-time adaptive adjustment of human-machine control weights under complex en-vironments,significantly improving trajectory smoothness and task completion efficiency.These results vali-date the feasibility and effectiveness of the proposed shared control strategy for brain-controlled robotic sys-tems.关键词
稳态运动视觉诱发电位/脑机接口/共享控制/模糊Q学习/移动机械臂Key words
steady-state motion visual evoked potential/brain-computer interface/shared control/fuzzy Q-learning/mobile manipulator分类
信息技术与安全科学引用本文复制引用
彭观辉,方慧娟,李真涵,罗继亮..融合模糊Q学习的脑控机器人共享控制策略[J].华侨大学学报（自然科学版）,2026,47(1):93-103,11.基金项目
福建省中央引导地方科技发展专项项目(2022L2012) （2022L2012）
