Robotic rehabilitation therapy has become an important technology to recover the motor ability of disabled individuals. Clinical studies indicate that involving the active intention of patient into rehabilitation training contributes to promoting the performance of therapies. An adaptive neural cooperative control strategy is developed in this letter to realize intention-based human-cooperative rehabilitation training. The human motion intention is estimated by fusing the human-robot interaction forces and the muscular forces into a Gaussian radial basis function network. A biological force estimation method is proposed to obtain the muscular forces of biceps and triceps based on surface electromyography signals and Kalman filter. A robust adaptive sliding mode controller is integrated into the cooperative control scheme to ensure the accuracy and stability of inner position control loop with uncertainties. The minimum jerk cost principle is used to improve the smoothness and continuity of trajectory. To evaluate the effectiveness of the proposed control scheme, further experimental investigations are conducted on a planar upper-limb rehabilitation robot with ten volunteers. The results indicate that the proposed control strategy has significant potential to modulate the interaction compliance and cooperation process during training.

中文翻译：

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基于意图的上肢机器人康复自适应神经协同控制策略的开发

机器人康复治疗已成为恢复残疾人运动能力的重要技术。临床研究表明，将患者的积极意图纳入康复训练有助于提高治疗效果。在这封信中开发了一种自适应神经协作控制策略，以实现基于意图的人机协作康复训练。通过将人机交互力和肌肉力融合成高斯径向基函数网络来估计人体运动意图。提出了一种基于表面肌电信号和卡尔曼滤波器的生物力估计方法来获得二头肌和三头肌的肌肉力。将鲁棒自适应滑模控制器集成到协同控制方案中，以确保内部位置控制回路在不确定性下的准确性和稳定性。最小加加速度成本原则用于提高轨迹的平滑性和连续性。为了评估所提出的控制方案的有效性，对具有 10 名志愿者的平面上肢康复机器人进行了进一步的实验研究。结果表明，所提出的控制策略在调节训练过程中的交互合规性和合作过程方面具有巨大的潜力。进一步的实验研究是在一个有 10 名志愿者的平面上肢康复机器人上进行的。结果表明，所提出的控制策略在调节训练期间的交互合规性和合作过程方面具有巨大的潜力。进一步的实验研究是在一个有 10 名志愿者的平面上肢康复机器人上进行的。结果表明，所提出的控制策略在调节训练期间的交互合规性和合作过程方面具有巨大的潜力。