Paper Info
Title
Robust and compliance control for robotic knee prosthesis using admittance model and sliding-mode controller
Abstract
Achieving compliance and flexibility under the premise of ensuring trajectory tracking performance and also reflecting the wearer's movement intention, has not yet been well solved in the field of prosthesis. The aim of this paper is to provide a compliant, robust, and continuous control scheme for robotic knee prosthesis to solve the contradictory problems of trajectory tracking performance and compliance. The proposed scheme are based on the admittance model and radial basis function (RBF) neural network-enhanced nonsingular fast terminal sliding-mode controller (NFTSMC). The desired trajectory of the prosthetic knee joint is driven by humans and reshaped to reference trajectory by an admittance model, so that the prosthetic leg can reflect the human's movement intention and being compliant. RBF neural network is introduced to achieve adaptive approximation of unknown models and ensure that the controller does not depend on the mathematical model of the "human-in-the-loop" prosthesis system. A novel NFTSMC was proposed to deal with the influence of ground reaction forces (GRFs) and fitting errors of the RBF neural network, which make the tracking error converge to zero in a finite time. The adaptive law of the RBF neural network is obtained by the Lyapunov method, and the stability and finite-time convergence of the closed-loop system are rigorously proved and analyzed mathematically. The simulation results prove the feasibility and effectiveness of the propose control scheme.
Year
DOI
Venue
2022
10.1177/01423312221088848
TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL
Keywords
DocType
Volume
Robotic knee prosthesis, admittance model, RBF neural network, terminal sliding mode
Journal
44
Issue
ISSN
Citations 
14
0142-3312
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Yongshan Huang100.34
xun23510.27
Quan He300.34
Lin Lang400.34
Honglei An500.34