摘要
为使患者的远程康复训练达到更为理想的效果,针对患者在训练时肌肉痉挛对双向遥操作系统的稳定性以及对从机械手的速度平滑性的影响,提出了一种新的基于BP神经网络变参数控制方法.通过检测患者训练时力、加速度、速度、位置等的变化,采用BP神经网络自动调整从端控制参数,从而消除了系统的不稳定性和减少了对系统平滑性的影响,并具有很强的鲁棒性.分析和仿真试验结果表明,此方法与传统的控制方法相比,可有效地克服患者因肌肉痉挛带来的干扰并具有较好的稳定性和平滑性.
During the tele-rehabilitation traing, the rehabilitant's muscle spasm may result in the bilateral telerobot system instability and cause its slave's speed unsmoothness, which make the rehabilitation training inefficient. In order to guarantee the stability and reduce the fluctuation of the speed, a new method based on the variable parameter with BP(back propagation) neural network was brought forward. By measuring the changes of the limb's force, acceleration, velocity and position, BP neural network automatically adjusted the control parameter of the slave side. Not only the stability can be guaranteed, but also the slave's speed unsmoothness can be decreased. The system has strong robustness. Analysis and simulation results show that this method is much more stable and smooth than the traditional control and can effectively restrain the interference resulting from rehabilitant's spasm.
出处
《东南大学学报(自然科学版)》
EI
CAS
CSCD
北大核心
2008年第1期54-57,共4页
Journal of Southeast University:Natural Science Edition
基金
国家高技术研究发展计划(863计划)资助项目(2006AA04Z246)
江苏省国际合作资助项目(BE2006046)
江苏省六大高峰人才资助项目(06-D-031)
关键词
远程康复
双向遥操作
BP神经网络
变参数
机器入
tele-rehabilitation
bilateral teleoperation
back propagation neural network
variable parameter
robot