Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1155/2017/5361246 | COMPLEXITY |
Field | DocType | Volume |
Nonlinear system,Coupling,Stiffness,Control theory,Feedback linearization,Parametric statistics,Adaptive control,Artificial neural network,Mathematics,Actuator | Journal | 2017 |
ISSN | Citations | PageRank |
1076-2787 | 4 | 0.50 |
References | Authors | |
19 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhao Guo | 1 | 55 | 11.51 |
| Yongping Pan | 2 | 50 | 4.64 |
| Tairen Sun | 3 | 41 | 3.81 |
| Yubing Zhang | 4 | 5 | 0.85 |
| Xiaohui Xiao | 5 | 23 | 13.56 |