Title :
Sensorless adaptive neural network control of permanent magnet synchronous motors
Author :
Liu, Tong ; Elbuluk, Malik ; Husain, Iqbal
Author_Institution :
Dept. of Electr. Eng., Akron Univ., OH, USA
Abstract :
Rotor speed and position estimations in permanent magnet synchronous motors (PMSM) suffer from accuracy due to variations of machine parameters such as torque constant, stator resistance and inductance, especially at low speeds. Also, conventional linear estimators are not adaptive. In this paper, two neural network-based model reference adaptive systems (MRAS) for position and speed estimation are presented for PMSM drives. The first network estimates the rotor speed and adapts online to any change in stator resistance. The second network estimates the rotor position and adapts online for any changes in the torque constant. A q-axis model for stator inductance based on current is used. In both cases, the MRAS adjusts the neural weights to give optimal performance over a wide speed range. Simulation results showed the estimation adjusts well to changes in the motor operating points
Keywords :
control system analysis; control system synthesis; machine vector control; model reference adaptive control systems; neurocontrollers; optimal control; parameter estimation; permanent magnet motors; position control; rotors; stators; synchronous motors; velocity control; control design; control simulation; model reference adaptive control systems; motor operating points; neural weights; optimal performance; permanent magnet synchronous motors; q-axis model; rotor position estimation; rotor speed estimation; sensorless adaptive neural network control; stator inductance; Adaptive control; Adaptive systems; Inductance; Neural networks; Permanent magnets; Programmable control; Rotors; Sensorless control; Stators; Torque;
Conference_Titel :
Electric Machines and Drives, 1999. International Conference IEMD '99
Conference_Location :
Seattle, WA
Print_ISBN :
0-7803-5293-9
DOI :
10.1109/IEMDC.1999.769094
