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

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

Research output: Contribution to conferencePaperpeer-review

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)
Number of pages1
StatePublished - 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

  • General Engineering


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