Prescribed performance model-free adaptive terminal sliding mode control for the pneumatic artificial muscles elbow exoskeleton

被引:0
|
作者
Zhirui Zhao
Lina Hao
Mingfang Liu
Haoze Gao
Xing Li
机构
[1] Northeastern University,School of Mechanical and Engineering
[2] Northeastern University,State Key Laboratory of Synthetical Automation for Process Industries
关键词
Elbow exoskeleton; Model-free adaptive control; Pneumatic artificial muscle; Prescribed performance control;
D O I
暂无
中图分类号
学科分类号
摘要
This paper focuses on the trajectory tracking issue of the pneumatic artificial muscle (PAM) exoskeleton system. First of all, a new type of the PAM elbow exoskeleton was introduced to assist wearers in elbow flexion/extension movement. Moreover, a model-free adaptive control approach was combined with the prescribed performance control to ensure the tracking errors to be converged to the predefined requirements. Meanwhile, to suffer the influence of the unknown external disturbance on the exoskeleton, a terminal sliding mode control was adopted to reduce the tracking errors. From a theoretical perspective, the stability of the proposed controller can be proved by Lyapunov synthesis. After two sets of experiments, the proposed control method can further improve the tracking accuracy in the PAM elbow exoskeleton, compared with the other three model-free adaptive control methods. Simultaneously, the maximum absolute value of the tracking errors never exceeded the designed boundary.
引用
收藏
页码:3183 / 3197
页数:14
相关论文
共 50 条
  • [1] Prescribed performance model-free adaptive terminal sliding mode control for the pneumatic artificial muscles elbow exoskeleton
    Zhao, Zhirui
    Hao, Lina
    Liu, Mingfang
    Gao, Haoze
    Li, Xing
    [J]. JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY, 2021, 35 (07) : 3183 - 3197
  • [2] Model-free adaptive iterative learning integral terminal sliding mode control of exoskeleton robots
    Esmaeili, Babak
    Madani, Seyedeh Sepideh
    Salim, Mina
    Baradarannia, Mahdi
    Khanmohammadi, Sohrab
    [J]. JOURNAL OF VIBRATION AND CONTROL, 2022, 28 (21-22) : 3120 - 3139
  • [3] Model-free adaptive integral sliding mode constrained control with modified prescribed performance
    Huang, Xiuwei
    Dong, Zhiyan
    Zhang, Feng
    Zhang, Lihua
    [J]. IET CONTROL THEORY AND APPLICATIONS, 2023, 17 (08): : 1044 - 1060
  • [4] Prescribed performance model-free adaptive sliding mode control of a shape memory alloy actuated system
    Liu, Mingfang
    Zhao, Zhirui
    Hao, Lina
    [J]. ISA TRANSACTIONS, 2022, 123 : 339 - 345
  • [5] Prescribed performance based model-free adaptive sliding mode constrained control for a class of nonlinear systems
    Zhang, Weiming
    Xu, Dezhi
    Jiang, Bin
    Pan, Tinglong
    [J]. INFORMATION SCIENCES, 2021, 544 : 97 - 116
  • [6] Adaptive Discrete Sliding Mode Control of a Pneumatic Artificial Muscles Robot
    Fellag, Ratiba
    Hamerlain, Mustapha
    Laghrouche, Salah
    Achour, Noura
    [J]. 2017 5TH INTERNATIONAL CONFERENCE ON ELECTRICAL ENGINEERING - BOUMERDES (ICEE-B), 2017,
  • [7] A model-free terminal sliding mode control for robots: Achieving fixed-time prescribed performance and convergence
    Truong, Thanh Nguyen
    Vo, Anh Tuan
    Kang, Hee -Jun
    [J]. ISA TRANSACTIONS, 2024, 144 : 330 - 341
  • [8] Prescribed Performance Function Based Sliding Mode Control Of Opposing Pneumatic Artificial Muscles To Enhance Safety
    Dinh, Van-Vuong
    Mai, Dinh-Hoang
    Duong, Minh-Duc
    Dao, Quy-Thinh
    [J]. JOURNAL OF APPLIED SCIENCE AND ENGINEERING, 2023, 27 (02): : 2117 - 2126
  • [9] Prescribed performance sliding mode control for the PAMs elbow exoskeleton in the tracking trajectory task
    Zhao, Zhirui
    Hao, Lina
    Tao, Guanghong
    Liu, Hongjun
    Shen, Lihua
    [J]. INDUSTRIAL ROBOT-THE INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH AND APPLICATION, 2024, 51 (01): : 167 - 176
  • [10] Prescribed Performance Model-Free Adaptive Integral Sliding Mode Control for Discrete-Time Nonlinear Systems
    Liu, Dong
    Yang, Guang-Hong
    [J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2019, 30 (07) : 2222 - 2230