Title :
Neural network control of a chopper-fed DC motor
Author :
Bates, John ; Elbuluk, Malik E. ; Zinger, Donald S.
Author_Institution :
Dept. of Electr. Eng., Akron Univ., OH, USA
Abstract :
Neural networks are finding their way into control of power electronics and electric machines. Neural network controllers (NCs) possess generalization and learning capabilities that makes them suitable for control of nonlinear, time-variant plants. Neural network control of a chopper-fed DC motor is studied. A dynamic NC is used in a direct adaptive control configuration. After proper training, the dynamic NC emulates the inverse plant dynamic mapping. Training requirements of a NC are investigated. Simulation results showing system performance are presented. Hardware implementation of the NC is discussed
Keywords :
DC motors; adaptive control; choppers (circuits); generalisation (artificial intelligence); learning (artificial intelligence); machine control; neurocontrollers; power engineering computing; chopper-fed DC motor; direct adaptive control; electric machines; inverse plant dynamic mapping; learning capabilities; neural network control; nonlinear plants; power electronics; simulation; time-variant plants; training; Adaptive control; Artificial neural networks; Biological neural networks; DC motors; Hardware; Nervous system; Neural networks; Neurons; Nonlinear dynamical systems; Uncertainty;
Conference_Titel :
Power Electronics Specialists Conference, 1993. PESC '93 Record., 24th Annual IEEE
Conference_Location :
Seattle, WA
Print_ISBN :
0-7803-1243-0
DOI :
10.1109/PESC.1993.472027
