Title :
A neuromorphic learning strategy for the control of a one-legged hopping machine
Author :
Helferty, J.J. ; Collins, John B. ; Kam, Moshe
Author_Institution :
Dept. of Electr. Eng., Temple Univ., Philadelphia, PA, USA
Abstract :
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.<>
Keywords :
adaptive control; learning systems; mobile robots; neural nets; dynamic systems; energy conservation; locomotive system; mobile robots; neural network; neuromorphic learning; one-legged hopping machine; Adaptive control; Learning systems; Mobile robots; Neural networks;
Conference_Titel :
Neural Networks, 1989. IJCNN., International Joint Conference on
Conference_Location :
Washington, DC, USA
DOI :
10.1109/IJCNN.1989.118499