[1]李 颖,孙容磊,张歆悦.基于自适应切换迭代学习的绳驱踝外骨骼力控制器设计[J].机械与电子,2024,42(09):36-44.
　LI Ying,SUN Ronglei,ZHANG Xinyue.Force-controller Design Based on Adaptively Switched Iterative Learning for Cable-driven Ankle Exoskeleton[J].Machinery & Electronics,2024,42(09):36-44.
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基于自适应切换迭代学习的绳驱踝外骨骼力控制器设计()
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《机械与电子》[ISSN:1001-2257/CN:52-1052/TH]

卷:
42
期数:
2024年09期
页码:
36-44
栏目:
自动控制与检测
出版日期:
2024-09-27

文章信息/Info

Title:
Force-controller Design Based on Adaptively Switched Iterative Learning for Cable-driven Ankle Exoskeleton
文章编号:
1001-2257 ( 2024 ) 09-0036-09
作者:
李 颖 1 ; 孙容磊 2 ; 张歆悦 3
1. 华中科技大学机械科学与工程学院,湖北 武汉 430074 ;
2. 华中科技大学医疗装备科学与工程研究院,湖北 武汉 430074 ;
3. 华中科技大学智能制造装备与技术全国重点实验室,湖北 武汉 430074
Author(s):
LI Ying1 ;  SUN Ronglei2 ;  ZHANG Xinyue3
( 1.School of Mechanical Science and Engineering , Huazhong University of Science and Technology , Wuhan 430074 , China ;
2.Institute of Medical Equipment Science and Engineering , Huazhong University of Science and Technology , Wuhan 430074 , China ;?
3.State Key Laboratory of Intelligent Manufacturing Equipment and Technology , Huazhong University of Science and Technology , Wuhan 430074 , China )
关键词:
自适应切换; 峰值迭代学习; PD 型迭代学习; 前馈补偿器; 足踝外骨骼; 鲍登绳传动
Keywords:
adaptively switching ;  peak iterative learning ;  PD-type iterative learning ;  feedforward controller ;  ankle exoskeleton ;  Bowden cable transmission
分类号:
TP242
文献标志码:
A
摘要:
人-外骨骼构成强耦合系统,步态的偶然变异对控制器产生较大干扰,导致系统震荡。针对这一问题,提出了一种自适应切换的迭代学习控制策略。在峰值误差较大时,采用峰值迭代学习控制,仅对力控信号峰值点进行迭代更新;当峰值误差较小时,切换为传统的 PD 型迭代学习控制,对力控信号所有点进行迭代更新。为进一步改善控制效果,设计了前馈控制器来补偿鲍登绳传动的非线性摩擦。仿真和外骨骼实验结果表明,与传统 PD 型迭代学习算法相比,自适应切换的迭代学习控制策略保证了助力曲线的平稳升降,同时该策略具有更快的收敛速度以及受干扰后更快的恢复速度,实现了行走过程中辅助力的准确跟踪控制。
Abstract:
The human-exoskeleton system is a strongly coupled system , and the incidental variation in gait will cause significant disturbance to the controller , resulting in oscillations within the system.To address this issue , an adaptive switching iterative learning control strategy was proposed.When the peak error value was large , a peak iterative learning control strategy was employed , which only iteratively updated the peak point of the force control signal.When the peak error is small , the control strategy switched to the conventional PD-type iterative learning control , which iteratively updated all points of the force control signal.To further improve the control effectiveness , a feedforward controller was designed to compensate for the nonlinear friction of the Bowden cable transmission.Simulation and experimental results on the exoskeleton platform show that our peak tracking iterative learning control strategy , compared to traditional PD-type algorithms , ensures smoother assistive force curve transitions.At the same time , this strategy has a faster?convergence? speed and a faster recovery speed after disturbances , achieving accurate tracking control of assistive force during walking process.

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备注/Memo

备注/Memo:
收稿日期: 2024-01-29
基金项目:国家自然科学基金资助项目( U21A20121 , 52027806 )
作者简介:李 颖 ( 1998- ),男,河北衡水人,硕士研究生,研究方向为外骨骼控制;孙容磊 ( 1963- ),男,湖北武汉人,教授,博士研究生导师,研究方向为机器人学、数字化康复与医疗装备、智能制造、机器学习,通信作者。
更新日期/Last Update: 2024-09-24