Conference proceeding
A neural network learning strategy for the control of a one-legged hopping machine
Proceedings, 1989 International Conference on Robotics and Automation, pp 1604-1609 vol.3
1989
Featured in Collection : UN Sustainable Development Goals @ Drexel
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 the mathematical model of the robot leg.< >
Metrics
14 Record Views
Details
- Title
- A neural network learning strategy for the control of a one-legged hopping machine
- Creators
- J.J Helferty - Temple UniversityJ.B Collins - Temple UniversityM Kam - Temple UniversityIEEE
- Publication Details
- Proceedings, 1989 International Conference on Robotics and Automation, pp 1604-1609 vol.3
- Publisher
- IEEE Comput. Soc. Press
- Resource Type
- Conference proceeding
- Language
- English
- Web of Science ID
- WOS:A1989BQ26M00242
- Other Identifier
- 991019346799404721
UN Sustainable Development Goals (SDGs)
This publication has contributed to the advancement of the following goals:
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Engineering, Electrical & Electronic
- Engineering, Mechanical