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Appl. Sci. 2018, 8(4), 525; doi:10.3390/app8040525

Probabilistic Sensitivity Amplification Control for Lower Extremity Exoskeleton

1
State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin 150080, China
2
School of Astronautics, Harbin Institute of Technology, Harbin 150080, China
3
Weapon Equipment Research Institute, China Ordnance Industries Group, Beijing 102202, China
*
Author to whom correspondence should be addressed.
Received: 27 January 2018 / Revised: 26 March 2018 / Accepted: 27 March 2018 / Published: 29 March 2018
(This article belongs to the Section Computer Science and Electrical Engineering)
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Abstract

To achieve ideal force control of a functional autonomous exoskeleton, sensitivity amplification control is widely used in human strength augmentation applications. The original sensitivity amplification control aims to increase the closed-loop control system sensitivity based on positive feedback without any sensors between the pilot and the exoskeleton. Thus, the measurement system can be greatly simplified. Nevertheless, the controller lacks the ability to reject disturbance and has little robustness to the variation of the parameters. Consequently, a relatively precise dynamic model of the exoskeleton system is desired. Moreover, the human-robot interaction (HRI) cannot be interpreted merely as a particular part of the driven torque quantitatively. Therefore, a novel control methodology, so-called probabilistic sensitivity amplification control, is presented in this paper. The innovation of the proposed control algorithm is two-fold: distributed hidden-state identification based on sensor observations and evolving learning of sensitivity factors for the purpose of dealing with the variational HRI. Compared to the other state-of-the-art algorithms, we verify the feasibility of the probabilistic sensitivity amplification control with several experiments, i.e., distributed identification model learning and walking with a human subject. The experimental result shows potential application feasibility. View Full-Text
Keywords: stochastic optimization; constraint handling; human-robot interaction (HRI); sensitivity amplification control (SAC); force control; exoskeleton stochastic optimization; constraint handling; human-robot interaction (HRI); sensitivity amplification control (SAC); force control; exoskeleton
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Wang, L.; Du, Z.; Dong, W.; Shen, Y.; Zhao, G. Probabilistic Sensitivity Amplification Control for Lower Extremity Exoskeleton. Appl. Sci. 2018, 8, 525.

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