Print ISSN: 1681-6900

Online ISSN: 2412-0758

Keywords : Neural Estimation

Sensorless Vector Control of Three Phase Induction Motor Based on Full Neural Estimator and Controller

Fatma H. Faris

Engineering and Technology Journal, 2011, Volume 29, Issue 9, Pages 1737-1750

Conventional vector control of A.C drives are widely used in industry and
many other applications, where high dynamic performance is required, this type of
controller usually needs costly speed sensor. Sensorless vector control is strongly
recommended in the dangerous sites and hostile environment, also to reduce cost
and increase reliability. In this way the rotor speed can be estimated from the
terminal voltage and current by means of DSP microprocessor. The DSP-estimator
is very complex hardware, has many operation problems, and very sensitive to the
electromagnetic interference (EMI). This paper proposes using the Artificial
Neural Network (ANN) to estimate the rotor speed, flux vector, torque, and unit
vectors instead of DSP-estimator. Also, the neural-based controller is proposed
too. The ordinary vector control with speed sensor and sensorless vector control
based on DSP-estimator PI-controller are represented in this work as point of
comparison. Also, the mathematical representation and simulation of the three
phase induction motor is illustrated in this paper. The proposed method, neuralbased
sensorless vector controller and estimator, gives superior performance in
different speed with respect to DSP-estimator PI-controller.

Field Oriented Control For Three Phase Induction Motor Based On Full Neural Estimator And Controller

Fadhil A. Hassan

Engineering and Technology Journal, 2010, Volume 28, Issue 15, Pages 5014-5027

Closed loop speed control for an I.M is somewhat complex strategy, the
complexity is gradually increases according to the demand performance degree. There are many types of control strategies: scalar, direct torque, adaptive, sensor less, and vector or Field Oriented Control (FOC). This paper proposes the FOC strategy in details. Rotor flux, unit vector, and electromagnetic torque estimation are considered by using Digital Signal Processing (DSP). Artificial Neural Network (ANN) becomes a powerful tool for control nonlinear system in present
time. This study proposes the using of ANN in stead of DSP to estimate the flux, unit vector, and electromagnetic torque to reduce the hardware complexity and the Electromagnetic Interference (EMI) effect. Also, it proposes the PI neural-based controller. The overall system simulation for both DSP and ANN are proposed. The performances of both systems are investigated, which give in DSP: rise time 0.24 sec, settling time 0.29 sec, overshot 5%, steady state error 0.5%. Whereas, in ANN: rise time 0.18, settling time 0.19 sec, overshot 1%, steady state error 0.2%.