Fixed-time constrained acceleration reconstruction scheme for robotic exoskeleton via neural networks

Tao XUE; Zi-wei WANG; Tao ZHANG; Ou BAI; Meng ZHANG; Bin HAN

doi:10.1631/FITEE.1900418

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Fixed-time constrained acceleration reconstruction scheme for robotic exoskeleton via neural networks

Special Issue on Intelligent Unmanned Systems | Updated：2022-05-19

- Fixed-time constrained acceleration reconstruction scheme for robotic exoskeleton via neural networks
- 基于神经网络的固定时间约束下外骨骼机器人加速度重构方法
- Frontiers of Information Technology & Electronic Engineering Vol. 21, Issue 5, Pages: 705-722(2020)
- Affiliations：
  
  Department of Automation, Tsinghua University, Beijing 100084, China
  Department of Electrical and Computer Engineering, Florida International University, Miami 33174, USA
  Move Robotics Technology Co., Ltd., Shanghai 201306, China
  School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
- Author bio：
  
  Tao ZHANG, E-mail: taozhang@tsinghua.edu.cn
- Funds：
- DOI：10.1631/FITEE.1900418
  CLC： TP242
- Received：21 August 2019，
  
  Revised：2019-;4-;7，
  
  Published：2020-05
- Accepted：
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Tao XUE, Zi-wei WANG, Tao ZHANG, et al. Fixed-time constrained acceleration reconstruction scheme for robotic exoskeleton via neural networks[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(5): 705-722.
DOI：

Tao XUE, Zi-wei WANG, Tao ZHANG, et al. Fixed-time constrained acceleration reconstruction scheme for robotic exoskeleton via neural networks[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(5): 705-722. DOI： 10.1631/FITEE.1900418.

摘要

精准的加速度信号采集对机械外骨骼系统十分重要，但其难以通过传感器系统直接测量。现有基于重构算法的加速度获取方法能够保证重构误差的有限时间收敛和扰动抑制，但忽略了误差约束和初始状态无关方法。为解决该问题，提出一种基于新型径向基神经网络的误差约束下的固定时间重构算法，以实现高性能的加速度信号估计。在该算法中，提出一种新型指数型障碍李雅普诺夫函数处理误差约束问题，该函数提供一种统一简洁的李雅普诺夫稳定性证明模板。与此同时，设计一种分数阶滑模控制律，以实现固定时间收敛；为进一步提升系统鲁棒性，使用自适应权重矩阵构建的径向基神经网络近似和消除完全未知的扰动。值得注意的是，该框架下误差的收敛时间与初始状态以及扰动无关，只取决于预设参数，并且重构误差始终位于预定义的界内。数值仿真实验和人体实验结果验证了本文方法的优点以及在实际场景中的鲁棒性。

Abstract

Accurate acceleration acquisition is a critical issue in the robotic exoskeleton system

but it is difficult to directly obtain the acceleration via the existing sensing systems. The existing algorithm-based acceleration acquisition methods put more attention on finite-time convergence and disturbance suppression but ignore the error constraint and initial state irrelevant techniques. To this end

a novel radical bias function neural network (RBFNN) based fixed-time reconstruction scheme with error constraints is designed to realize high-performance acceleration estimation. In this scheme

a novel exponential-type barrier Lyapunov function is proposed to handle the error constraints. It also provides a unified and concise Lyapunov stability-proof template for constrained and non-constrained systems. Moreover

a fractional power sliding mode control law is designed to realize fixed-time convergence

where the convergence time is irrelevant to initial states or external disturbance

and depends only on the chosen parameters. To further enhance observer robustness

an RBFNN with the adaptive weight matrix is proposed to approximate and attenuate the completely unknown disturbances. Numerical simulation and human subject experimental results validate the unique properties and practical robustness.

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