Neuromorphic learning strategy for the control of a one-legged hopping machine

John J. Helferty, Joseph B. Collins, Moshe Kam

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


Summary form only given, as follows. An adaptive, neural network strategy is described for the control of a dynamic, locomotive system, in particular a one-legged hopping robot. The control task is to make corrections to the motion of the robot that serve to maintain a fixed level of energy (and minimize energy losses). While for many dynamic systems energy conservation may not be a key control criterion, legged locomotion is an energy intensive activity, implying that energy conservation is a primary issue in control considerations. The authors effect the control of the robot by the use of an artificial neural network (ANN) with a continuous learning memory. Results are presented in the form of computer simulations that demonstrate the ANN's ability to devise proper control signals that will develop a stable hopping strategy using imprecise knowledge of the current state of the robotic leg.

Original languageEnglish (US)
Title of host publicationIJCNN Int Jt Conf Neural Network
Editors Anon
PublisherPubl by IEEE
Number of pages1
StatePublished - Dec 1 1989
Externally publishedYes
EventIJCNN International Joint Conference on Neural Networks - Washington, DC, USA
Duration: Jun 18 1989Jun 22 1989


OtherIJCNN International Joint Conference on Neural Networks
CityWashington, DC, USA

All Science Journal Classification (ASJC) codes

  • Engineering(all)


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