Title :
A novel speed identification method of induction motor based ANFIS
Author :
Bingzhe Han ; Honglin OuYang ; Yanhui Lv ; Long Tang
Author_Institution :
Coll. of Electr. & Inf. Eng., Hunan Univ., Changsha, China
Abstract :
One of the most important technologies for electric vehicles is the drive control technology which does not require a position or speed sensor. In a sensorless vector controlled induction machine, the speed must be indentified from the system. Most of the sensorless control techniques are dependent on motor parameters, such as stator resistance, inductance, and torque constant. Thus, the performance suffers greatly in harsh and highly dynamic operating conditions, where the motor parameters are changing. Model Reference Adaptive System (MRAS) based techniques are one of the best methods to estimate the rotor speed due to its performances and straight forward stability approach. But, the performance of MRAS deteriorates during transients, speed variations and load variations due to integrator drift and sensitivity to parameter variation. In this paper, a novel strategy of adaptive network-based fuzzy inference system (ANFIS) sensorless observers based on model reference adaptive system (MRAS) is proposed. This strategy replaces the traditional model of MRAS adaptive architecture and adaptive machine with ANFIS controller which aims at well adapting to changes in parameters and states by making use of ANFIS adaptive ability and self-learning ability in non-linear system. Further, a detailed comparative simulation and experimental study is carried out for ANFIS and conventional MRAS observers. The experimental results demonstrate that the proposed strategy is effective and has practical value.
Keywords :
electric vehicles; estimation theory; fuzzy control; fuzzy reasoning; induction motor drives; model reference adaptive control systems; nonlinear control systems; observers; position control; sensitivity; sensorless machine control; stability; stators; velocity control; ANFIS adaptive ability; ANFIS controller; ANFIS sensorless observers; MRAS adaptive architecture; MRAS based technique; adaptive machine; adaptive network-based fuzzy inference system sensorless observers; comparative simulation; drive control technology; electric vehicles; inductance; induction motor; integrator drift; model reference adaptive system based technique; motor parameters; nonlinear system; position sensor; rotor speed estimation; self-learning ability; sensitivity; sensorless control technique; sensorless vector controlled induction machine; speed identification method; speed sensor; stator resistance; straight forward stability approach; torque constant; Adaptation models; Adaptive systems; Induction motors; Mathematical model; Observers; Rotors; ANFIS; Adaptive control; Induction motor; MRAS;
Conference_Titel :
Chinese Automation Congress (CAC), 2013
Conference_Location :
Changsha
Print_ISBN :
978-1-4799-0332-0
DOI :
10.1109/CAC.2013.6775701