Title :
Gaussian networks for control of a class of systems with friction
Author :
Du, Hongliu ; Vargas, Victor ; Nair, Satish S.
Author_Institution :
Dept. of Mech. & Aerosp. Eng., Missouri Univ., Columbia, MO, USA
Abstract :
Identification and stable adaptive control of a class of systems with friction is considered using Gaussian networks. Preliminary results are presented for a proposed strategy using an experimental system consisting of a DC motor and a load. The nonlinearity due to friction, which is significant at low velocities, is first identified using a Gaussian network and then compensated for using the network in a feedforward mode. The Gaussian network is shown to have a `general´ structure suited for friction problems. The network development takes advantage of the constructive methodology for generating stable adaptive laws for Gaussian networks proposed by Sanner and Slotine (1993)
Keywords :
DC motors; adaptive control; compensation; dynamics; feedforward neural nets; force control; friction; identification; neurocontrollers; robust control; DC motor; Gaussian networks; adaptive control; compensation; feedforward neural networks; friction; identification; mechanical systems; robust control; Adaptive control; Aerospace engineering; Computer networks; Control systems; DC motors; Friction; Laboratories; Mechanical systems; Neural networks; Position control;
Conference_Titel :
Control Applications, 1996., Proceedings of the 1996 IEEE International Conference on
Conference_Location :
Dearborn, MI
Print_ISBN :
0-7803-2975-9
DOI :
10.1109/CCA.1996.558634