Title :
Position control of a PM stepper motor using neural networks
Author_Institution :
Sch. of Electr. Eng., New South Wales Univ., Sydney, NSW, Australia
Abstract :
Considers position control of a PM stepper motor. A control scheme is proposed based on a kind of exact linearization controller and a neural network based compensating controller. This scheme takes advantage of the simplicity of the model based control approach and uses the neural network controller to compensate for the motor modeling uncertainties. The neural network is trained online based on Lyapunov theory and thus its convergence is guaranteed
Keywords :
Lyapunov methods; closed loop systems; compensation; convergence; linearisation techniques; machine control; neurocontrollers; permanent magnet motors; position control; radial basis function networks; stepping motors; Lyapunov theory; PM stepper motor; exact linearization controller; model based control approach; motor modeling uncertainties; neural network based compensating controller; Artificial neural networks; Control systems; DC motors; Feedback control; Induction motors; Neural networks; Position control; Rotors; Torque; Uncertainty;
Conference_Titel :
Decision and Control, 2000. Proceedings of the 39th IEEE Conference on
Conference_Location :
Sydney, NSW
Print_ISBN :
0-7803-6638-7
DOI :
10.1109/CDC.2000.912117
