Full Text:   <246>

Summary:  <76>

CLC number: TP242

On-line Access: 2020-05-18

Received: 2019-08-21

Revision Accepted: 2019-12-05

Crosschecked: 2020-04-07

Cited: 0

Clicked: 474

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Tao Zhang

https://orcid.org/0000-0002-2980-6281

Tao Xue

https://orcid.org/0000-0001-7499-6160

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Frontiers of Information Technology & Electronic Engineering  2020 Vol.21 No.5 P.705-722

http://doi.org/10.1631/FITEE.1900418


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


Author(s):  Tao Xue, Zi-wei Wang, Tao Zhang, Ou Bai, Meng Zhang, Bin Han

Affiliation(s):  Department of Automation, Tsinghua University, Beijing 100084, China; more

Corresponding email(s):   taozhang@tsinghua.edu.cn

Key Words:  Acceleration reconstruction, Fixed-time convergence, Constrained control, Barrier Lyapunov function, Initial state irrelevant technique, Robotic exoskeleton


Tao Xue, Zi-wei Wang, Tao Zhang, Ou Bai, Meng Zhang, Bin Han. Fixed-time constrained acceleration reconstruction scheme for robotic exoskeleton via neural networks[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(5): 705-722.

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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.

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

薛涛1,王子威1,张涛1,白鸥2,张萌3,韩斌4
1清华大学自动化系,中国北京市,100084
2佛罗里达国际大学电气与计算机工程系,美国迈阿密,33174
3上海博灵机器人科技有限责任公司,中国上海市,201306
4华中科技大学机械科学与工程学院,中国武汉市,430074

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。

关键词:加速度重构;固定时间收敛;约束控制;障碍李雅普诺夫函数;初始状态无关方法;外骨骼机器人

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