Neural network 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

7 Scopus citations

Abstract

Results are presented on two neural network strategies for the control of dynamic, locomotive systems, 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), which yields a stable periodic limit cycle in the system's state space. Control of the robot is achieved by the use of artificial neural networks (ANNs) with a continuous learning memory. Through continuous reinforcement for past successes and failures, the control system develops a stable strategy for accomplishing the desired control objectives. The results are presented in the form of computer simulation that demonstrate the ability of two different ANNs to devise proper control signals that will develop a stable hopping strategy, and hence a stable limit cycle in the robot's state space, using imprecise knowledge of both the current state and mathematical model of the robot leg.

Original languageEnglish (US)
Pages1604-1609
Number of pages6
StatePublished - Dec 1 1989
Externally publishedYes
EventIEEE International Conference on Robotics and Automation - 1989 - Scottsdale, AZ, USA
Duration: May 14 1989May 19 1989

Other

OtherIEEE International Conference on Robotics and Automation - 1989
CityScottsdale, AZ, USA
Period5/14/895/19/89

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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