Title :
Brushless DC motor control using a general regression neural network
Author :
Patton, James B.
Author_Institution :
Dept. of Electr. & Comput. Eng., Maine Univ., Orono, ME, USA
Abstract :
A general regression neural network (GRNN) is used to simulate the speed control of a brushless DC motor. The GRNN offers advantages over conventional controllers in terms of adaptability, robustness to parameter variation, and simplicity. Because of its capability to learn quickly, the GRNN is particularly suited to online learning of changing plant conditions
Keywords :
adaptive control; brushless DC motors; control system analysis computing; control system synthesis; electric machine analysis computing; learning (artificial intelligence); machine control; machine theory; neurocontrollers; robust control; adaptability; brushless DC motor control; changing plant conditions; computer simulation; control design; control simulation; general regression neural network; online learning; parameter variation; robustness; simplicity; Adaptive control; Brushless DC motors; Computational modeling; Computer simulation; DC motors; Feedforward neural networks; Feeds; Neural networks; Robustness; Velocity control;
Conference_Titel :
Industrial Electronics, Control, and Instrumentation, 1995., Proceedings of the 1995 IEEE IECON 21st International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-3026-9
DOI :
10.1109/IECON.1995.484159
