Adaptive iterative learning control of flexible ankle rehabilitation robot

被引：0
作者：
Liu Q. [1 ]
Xie X. [1 ]
Meng W. [1 ]
Ai Q. [1 ]
机构：
[1] School of Information Engineering, Wuhan University of Technology, Wuhan
来源：
Huazhong Keji Daxue Xuebao (Ziran Kexue Ban)/Journal of Huazhong University of Science and Technology (Natural Science Edition) | 2023年 / 51卷 / 05期
关键词：
dynamic linearization; flexible rehabilitation robot; iterative learning control; pseudo-partial derivative; zeroing neural network;
D O I：
10.13245/j.hust.230509
中图分类号：
学科分类号：
摘要：
To solve the problem that model-free adaptive iterative learning control (MFAILC) in the practical control of ankle rehabilitation robot driven by pneumatic muscles could lead to slow convergence and poor control performance under system noise interference or improper selection of the initial pseudo-partial derivative (PPD)，a model-free iterative learning control method based on high-order PPD tuning was proposed，and the control law of MFAILC based on the zeroing neural network (ZNN) error recursion was designed．The system observation data was introduced to modify the initial PPD and to reduce the influence of the selection of initial PPD on the convergence speed．The noise tolerant ZNN control law was designed to reduce the influence of system noise on control performance，enabling the high-performance trajectory tracking of flexible rehabilitation robot in noise environment．Simulation results show that the maximum tracking error can be reduced by fewer iterations in noise environments． The actual control results of robot show that the average tracking error of pneumatic muscle can be reduced within 2% after 7 iterations，and the superior convergence and trajectory tracking performance can be achieved under different initial PPDs. © 2023 Huazhong University of Science and Technology. All rights reserved.
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页码：53 / 59
页数：6
相关论文
共 16 条
  • [1] KHALID Y M，, GOUWANDA D，, PARASURAMAN S．, A review on the mechanical design elements of ankle rehabilitation robot[J], Proc of the Institution of Mechanical Engineers, 229, 6, pp. 452-463, (2015)
  • [2] HSIAO J H, CHANG J Y, CHENG C M．, Soft medical robotics：clinical and biomedical applications，challenges， and future directions[J], Advanced Robotics, 33, 21, pp. 1099-1111, (2019)
  • [3] 30, 6, pp. 793-802, (2015)
  • [4] LIU Q, LIU A, MENG W, Hierarchical compliance control of a soft ankle rehabilitation robot actuated by pneumatic muscles[J], Frontiers in Neurorobotics, 11, pp. 64-82, (2017)
  • [5] 50, 1, pp. 43-53, (2021)
  • [6] MENG W, XIE S Q, LIU Q, Robust iterative feedback tuning control of a compliant rehabilitation robot for repetitive ankle training[J], IEEE/ASME Transactions on Mechatronics, 22, 1, pp. 173-184, (2016)
  • [7] 29, 8, pp. 1001-1009, (2012)
  • [8] YU Q，, HOU Z，, BU X, RBFNN-based data-driven predictive iterative learning control for nonaffine nonlinear systems[J], IEEE Transactions on Neural Networks and Learning Systems, 31, 4, pp. 1170-1182, (2019)
  • [9] CHI R，, HOU Z，, JIN S, Computationally efficient data-driven higher order optimal iterative learning control[J], IEEE Transactions on Neural Networks and Learning Systems, 29, 12, pp. 5971-5980, (2018)
  • [10] CHI R, HUANG B，, HOU Z, Data-driven high-order terminal iterative learning control with a faster convergence speed [J], International Journal of Robust Nonlinear Control, 28, 1, pp. 103-119, (2018)
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