There is a growing interest in electric vehicles due to environmental concerns. Recent efforts are directed toward developing an improved propulsion system for electric vehicles applications. This paper is aimed at developing the system design philosophies of ANFIS observer as speed sensor .Now the induction motor is growing up to use as propulsion in the electric vehicles. The simple method to develop observer for detects the speed of Induction motor as propulsion in the electric vehicle is proposed in this paper, vector control method is one’s of the method which developing in this paper. Direct field-oriented induction motor drive system need rotor flux observer and rotor angular speed identifier. ANFIS is used for identifying parameter dynamics and system variable estimation, linear either non-linear. ANFIS with back propagation learning algorithm has applied to estimate flux rotor and identify rotor angular speed of three-phase induction motor. The simulation result is found well for the speed up to 200 rpm, and no good result for the speed less than 200 rpm. There is a growing interest in electric vehicles due to environmental concerns. Recent efforts are directed toward developing an improved propulsion system for electric vehicles applications. This paper is aimed at developing the system design philosophies of ANFIS observer as speed sensor. Now the induction motor is growing up to use as propulsion in the electric vehicles. The simple method to develop observer for detects the speed of Induction motor as propulsion in the electric vehicles are proposed in this paper, direct field-oriented is one’s of the method which developing in this paper. Direct fieldoriented induction motor drive system need rotor flux observer and rotor angular speed identifier. ANFIS is used for identifying parameter dynamics and system variable estimation, linear either nonlinear. ANFIS with back propagation learning algorithm has applied to estimate flux rotor and identify rotor angular speed of three-phase induction motor. The simulation result is found well for the speed up to 200 rpm and no good result for the speed less than 200 rpm.
[1]
Yang Yong,et al.
On speed sensorless vector control system for induction motor based on estimating speed by torque current differential
,
2008,
2008 27th Chinese Control Conference.
[2]
J. G. Khor,et al.
Neural and Fuzzy Logic Control of Drives and Power Systems
,
2002
.
[3]
S. Nasar,et al.
Vector control of AC drives
,
1992
.
[4]
Jyh-Shing Roger Jang,et al.
Self-learning fuzzy controllers based on temporal backpropagation
,
1992,
IEEE Trans. Neural Networks.
[5]
Stephen L. Chiu,et al.
Fuzzy Model Identification Based on Cluster Estimation
,
1994,
J. Intell. Fuzzy Syst..
[6]
K. Matsuse,et al.
Speed sensorless field oriented control of induction motor with rotor resistance adaptation
,
1993,
Conference Record of the 1993 IEEE Industry Applications Conference Twenty-Eighth IAS Annual Meeting.
[7]
Zhao Zhengming,et al.
Speed sensorless control for three-level inverter-fed induction motors using an Extended Luenberger Observer
,
2008,
2008 IEEE Vehicle Power and Propulsion Conference.
[8]
Murat Barut,et al.
Speed-Sensorless Estimation for Induction Motors Using Extended Kalman Filters
,
2007,
IEEE Transactions on Industrial Electronics.
[9]
B. Karanayil,et al.
Online Stator and Rotor Resistance Estimation Scheme Using Artificial Neural Networks for Vector Controlled Speed Sensorless Induction Motor Drive
,
2007,
IEEE Transactions on Industrial Electronics.