基于屏障 Lyapunov 函数的上肢康复机器人自适应主动交互训练控制

针对上肢运动功能障碍患者进行辅助康复训练，搭建了一套上肢康复外骨骼机器人系统，并提出一种基于屏障Lyapunov函数的增广神经网络自适应导纳控制策略。首先，介绍了上肢康复外骨骼的机械机构及其控制系统。然后，推演了控制器的设计过程并进行了Lyapunov稳定性证明。最后，分别进行了不同控制内环的轨迹跟踪被动训练实验和不同导纳参数下基于人机交互力的主动交互训练实验，同时分析比对了主动训练时的人机交互力与轨迹偏差的变化关系。被动训练实验结果证明了增广神经网络对人机模型动力学的逼近效果，其轨迹跟踪峰值误差为模糊PID控制器的53%。主动交互训练实验证明了通过调整导纳参数可实现在相同训练任务下不同强度的康复训练以匹配不同康复阶段下的患者。

Adaptive active interaction exercise control of upper limb rehabilitation robotbased on the barrier Lyapunov function

To assist patients with upper limb motor dysfunction for rehabilitation training, an upper limb rehabilitation exoskeleton robotsystem is established and an augmented neural network adaptive admittance control strategy based on the barrier Lyapunov function isproposed. Firstly, the mechanical mechanism and control system of upper limb rehabilitation exoskeleton are introduced. Then, thedesign process of the controller is illustrated and Lyapunov stability is demonstrated. Finally, the passive training experiment of trajectorytracking with different inner control loops and the active interaction training experiment based on human-robot interaction force underdifferent admittance parameters are carried out. Experimental results of passive training show that the effectiveness of the augmentedneural network is close to human-robot dynamics, and the maximum trajectory tracking error is only 53% of that of fuzzy PID controller.The active interaction training experiment proves that different intensities of rehabilitation training can be achieved under the sametraining task by adjusting the admittance parameters to match patients with different levels of recovery.