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Volume 2017, Article ID 5361246, 9 pages
Research Article

Adaptive Neural Network Control of Serial Variable Stiffness Actuators

1School of Power and Mechanical Engineering, Wuhan University, Wuhan 430072, China
2Department of Biomedical Engineering, National University of Singapore, Singapore
3National University of Singapore (Suzhou) Research Institute, Suzhou 215123, China
4Department of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China

Correspondence should be addressed to Yongping Pan; moc.liamg@nappgnoy

Received 13 August 2017; Revised 8 September 2017; Accepted 20 September 2017; Published 8 November 2017

Academic Editor: Junpei Zhong

Copyright © 2017 Zhao Guo et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper focuses on modeling and control of a class of serial variable stiffness actuators (SVSAs) based on level mechanisms for robotic applications. A multi-input multi-output complex nonlinear dynamic model is derived to fully describe SVSAs and the relative degree of the model is determined accordingly. Due to nonlinearity, high coupling, and parametric uncertainty of SVSAs, a neural network-based adaptive control strategy based on feedback linearization is proposed to handle system uncertainties. The feasibility of the proposed approach for position and stiffness tracking of SVSAs is verified by simulation results.