Name
Abstract | ||
|---|---|---|
Virtual model control (VMC) has previously been successfully applied to steady dynamic walking of a planar biped. This control methodology requires very low computation because it does not calculate the inverse dynamics of the biped. An adaptive control approach based on radial basis function neural networks (RBFNNs) has also been previously proposed to enhance VMC. However, such implementation is computationally intensive. We propose a simpler adaptive VMC that allows the biped to adapt to mass variations without using RBFNNs. We implement the resulting system and demonstrate the robustness of the implementation by simulating the biped walking over rolling terrain |
Year | DOI | Venue |
|---|---|---|
1999 | 10.1109/IROS.1999.811686 | IROS |
Keywords | Field | DocType |
robustness,robot dynamics,steady dynamic walking,minimum model adaptive control,virtual model control,robust control,planar biped,legged locomotion,spatial variables control,velocity control,adaptive control,mass variations,rolling terrain,robots,inverse dynamics,leg,actuators,computational modeling | Control theory,Computer science,Terrain,Control engineering,Robustness (computer science),Inverse dynamics,Adaptive control,Robust control,Robot,Computation,Actuator | Conference |
Volume | ISBN | Citations |
3 | 0-7803-5184-3 | 2 |
PageRank | References | Authors |
1.78 | 5 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chee-Meng Chew | 1 | 375 | 40.58 |
| gill a pratt | 2 | 721 | 116.12 |