• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊

计算机工程与科学

• 人工智能与数据挖掘 • 上一篇    下一篇

超前采样时间迭代学习的下肢康复机器人轨迹跟踪控制

于振中,谷华航   

  1. (江南大学轻工过程先进控制教育部重点实验室,江苏 无锡 214122)
  • 收稿日期:2019-03-01 修回日期:2019-04-24 出版日期:2019-10-25 发布日期:2019-10-25

Trajectory tracking control of lower limb rehabilitation robots
based on iterative learning with advanced sampling time

YU Zhen-zhong,GU Hua-hang   

  1. (Key Laboratory of Advanced Control of Light Industry Process,
    Ministry of Education,Jiangnan University,Wuxi 214122,China)
  • Received:2019-03-01 Revised:2019-04-24 Online:2019-10-25 Published:2019-10-25

摘要:

为了实现下肢康复机器人在康复训练过程中高精度的末端轨迹跟踪控制,提出了一种利用超前采样时间的鲁棒自适应迭代学习控制方法。所述超前采样时间迭代算法,是指利用之前运行批次在t+Δ采样时刻的髋膝关节力矩输出,优化调整下一次运行时刻t处的关节力矩给定。仿真结果表明,采用超前采样时间迭代控制,末端轨迹误差具有更快的收敛速度和跟踪精度,并且具有较好的抗干扰性能。
 

关键词: 下肢康复机器人, 超前采样时间, 迭代学习, 轨迹跟踪

Abstract:

In order to realize a high-precision end trajectory tracking control of lower limb rehabilitation robots in the process of rehabilitation training, we propose a robust adaptive control method based on iterative learning with advanced sampling time. The iterative algorithm with advanced sampling time  uses the torque output of the hip and knee joint of the previous running batch at the sampling time  t+Δ,and optimizes the adjustment of the joint torque at the next running time t. Simulation results show that with the help of the iterative control with advanced sampling time, the end trajectory error has faster convergence speed, higher tracking accuracy, and better anti-interference performance.
 

Key words: lower limb rehabilitation robot, advanced sampling time, iterative learning, trajectory tracking