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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Adaptive Neural Control of a Kinematically Redundant Exoskeleton Robot Using Brain–Machine Interfaces
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Adaptive Neural Control of a Kinematically Redundant Exoskeleton Robot Using Brain–Machine Interfaces

机译：使用脑机接口的运动学冗余外骨骼机器人的自适应神经控制

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摘要

In this paper, a closed-loop control has been developed for the exoskeleton robot system based on brain-machine interface (BMI). Adaptive controllers in joint space, a redundancy resolution method at the velocity level, and commands that generated from BMI in task space have been integrated effectively to make the robot perform manipulation tasks controlled by human operator's electroencephalogram. By extracting the features from neural activity, the proposed intention decoding algorithm can generate the commands to control the exoskeleton robot. To achieve optimal motion, a redundancy resolution at the velocity level has been implemented through neural dynamics optimization. Considering human-robot interaction force as well as coupled dynamics during the exoskeleton operation, an adaptive controller with redundancy resolution has been designed to drive the exoskeleton tracking the planned trajectory in human brain and to offer a convenient method of dynamics compensation with minimal knowledge of the dynamics parameters of the exoskeleton robot. Extensive experiments which employed a few subjects have been carried out. In the experiments, subjects successfully fulfilled the given manipulation tasks with convergence of tracking errors, which verified that the proposed brain-controlled exoskeleton robot system is effective.
机译：本文研究了一种基于脑机接口（BMI）的外骨骼机器人系统的闭环控制。关节空间中的自适应控制器，速度级别的冗余解决方法以及从任务空间中的BMI生成的命令已有效集成，以使机器人执行由操作员的脑电图控制的操纵任务。通过从神经活动中提取特征，所提出的意图解码算法可以生成控制外骨骼机器人的命令。为了实现最佳运动，已经通过神经动力学优化实现了速度级别的冗余分辨率。考虑到人机交互作用以及外骨骼操作过程中的动力学耦合，已设计了具有冗余分辨率的自适应控制器，以驱动外骨骼跟踪人脑的计划轨迹，并提供一种方便的动力学补偿方法，而对基础知识的了解最少。外骨骼机器人的动力学参数。已经进行了使用几个主题的广泛实验。在实验中，受试者成功地完成了给定的操纵任务，并且跟踪误差趋于一致，这证明了所提出的脑控外骨骼机器人系统是有效的。

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