Title :
Neural networks for nonlocal hysteresis function identification and compensation
Abstract :
We discuss the online identification of nonlocal static hysteresis functions, which are encountered in mechanical friction, magnetic materials, and piezoelectric actuators and causes problems by the design of controllers. We introduce a compensation method for friction in presliding regime based on the simplified Leuven friction model and on technology borrowed from neural networks. We present a solution how to identify the hysteresis caused by the friction and how to use this identified model for the compensation of the friction effects. Results from both simulations and experiments are shown.
Keywords :
friction; identification; magnetic hysteresis; neural nets; physics computing; piezoelectric actuators; Leuven friction model; friction compensation method; magnetic material; mechanical friction; neural network; nonlocal hysteresis function; piezoelectric actuator; Actuators; Control systems; Friction; Information systems; Least squares approximation; Magnetic hysteresis; Magnetic materials; Mechanical engineering; Mechanical variables measurement; Neural networks;
Conference_Titel :
Intelligent Signal Processing, 2003 IEEE International Symposium on
Print_ISBN :
0-7803-7864-4
DOI :
10.1109/ISP.2003.1275831
