2026-09-18T21:45:02Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/477112021-07-05T14:03:20Zcom_1721.1_7582com_1721.1_7581col_1721.1_131024
Gill A. Pratt.
Hu, Jianjuen, 1964-
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2009-10-01T15:34:10Z
2009-10-01T15:34:10Z
1998
1998
http://hdl.handle.net/1721.1/47711
42363789
Thesis (Elec.E.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1998.
Includes bibliographical references (p. 90-94).
Stability and robustness are two important performance requirements for a dynamic walking robot. Learning and adaptation can improve stability and robustness. This thesis explores such an adaptation capability through the use of neural networks. Three neural network models (BP, CMAC and RBF networks) are studied. The RBF network is chosen as best, despite its weakness at covering high dimensional input spaces. To overcome this problem, a self-organizing scheme of data clustering is explored. This system is applied successfully in a biped walking robot system with a supervised learning mode. Generalized Virtual Model Control (GVMC) is also proposed in this thesis, which is inspired by a bio-mechanical model of locomotion, and is an extension of ordinary Virtual Model Control. Instead of adding virtual impedance components to the biped skeletal system in virtual Cartesian space, GVMC uses adaptation to approximately reconstruct the dynamics of the biped. The effectiveness of these approaches is proved both theoretically and experimentally (in simulation).
by Jianjuen Hu.
Elec.E.
94 p.
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by
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http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science
Learning control of bipedal dynamic walking robots with neural networks
Thesis
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