摘要
在患者进行遥康复训练时,肌肉痉挛会对遥康复训练系统稳定性及从机械手速度平滑性产生较大的影响。针对这些问题提出了一种新的基于BP神经网络辨识的变增益控制方法。通过BP神经网络实时辨识患肢动力学参数并根据其变化进行自适应调整控制增益,不仅消除了因患者肌肉痉挛带来的不稳定性,而且减少了其对系统运动平滑性的影响,可提高康复训练效果。分析和仿真试验结果表明,该控制方法与传统的控制方法相比,可有效地抑制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。
Rehabilitant's muscle spasm can make a tele-rehabilitation training system instable and cause its slave's movement to be unsmooth when a rehabilitant receives a tele-rehabilitation training. To solve these problems, this paper presents a new variable gain control technique based on the BP neural netowrk identification. The BP neural network can perform its real-time identification of limb's dynamic parameters and self-adapting control gains according to their changes, thus eliminating the system inslability and lessening the unsmoothness of the slave's movement as well as improving the rehabilitation training efficiency. Analysis and simulation results have proved this method can retrain the spasm interference effectivly and is much stabler and smoother than the conventional control.
出处
《工业仪表与自动化装置》
2008年第3期3-7,20,共6页
Industrial Instrumentation & Automation
基金
国家863计划资助项目(2006AA04Z246)
江苏省国际合作项目资助(BE2006046)
教育部重点项目资助(107053)
关键词
遥康复
痉挛
平滑性
力反馈遥操作
BP神经网络辨识
变增益
tele-rehabilitation
spasm
smoothness
force-feedback teleoperation
BP neural network identification :variable gain