Neural Network Based Contact Force Control Algorithm for Walking Robots

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Walking algorithms using push-off improve moving efficiency and disturbance rejection performance. However, the algorithm based on classical contact force control requires an exact model or a Force/Torque sensor. This paper proposes a novel contact force control algorithm based on neural networks. The proposed model is adapted to a linear quadratic regulator for position control and balance. The results demonstrate that this neural network-based model can accurately generate force and effectively reduce errors without requiring a sensor. The effectiveness of the algorithm is assessed with the realistic test model. Compared to the Jacobian-based calculation, our algorithm significantly improves the accuracy of the force control. One step simulation was used to analyze the robustness of the algorithm. In summary, this walking control algorithm generates a push-off force with precision and enables it to reject disturbance rapidly.
Publisher
MDPI
Issue Date
2021-01
Language
English
Article Type
Article
Citation

SENSORS, v.21, no.1, pp.287

ISSN
1424-8220
DOI
10.3390/s21010287
URI
http://hdl.handle.net/10203/280638
Appears in Collection
ME-Journal Papers(저널논문)
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