Induction motor control based on adaptive inverse control

2011 ◽  
Author(s):  
Liu Gang ◽  
Lina Yang
Keyword(s):  
Motor Control ◽  
Induction Motor ◽  
Adaptive Inverse Control ◽  
Inverse Control

A new Technique for Position Control of Induction Motor Using Adaptive Inverse Control

2010 ◽  
Vol 6 (2) ◽  
pp. 116-122
Author(s):  
Aamir Hashim Obeid Ahmed ◽  
Martino O. Ajangnay ◽  
Shamboul A. Mohamed ◽  
Matthew W. Dunnigan
Keyword(s):  
Induction Motor ◽  
Position Control ◽  
New Technique ◽  
Adaptive Inverse Control ◽  
Inverse Control ◽  
A New Technique

scholarly journals A new Technique for Position Control of Induction Motor Using Adaptive Inverse Control

2010 ◽  
Vol 6 (2) ◽  
pp. 116-122
Author(s):  
Aamir Ahmed ◽  
Martino Ajangnay ◽  
Shamboul Mohamed ◽  
Matthew Dunnigan
Keyword(s):  
Induction Motor ◽  
Position Control ◽  
Open Loop ◽  
Adaptive Inverse Control ◽  
Loop Control ◽  
Inverse Control ◽  
Control Scheme ◽  
Highly Nonlinear ◽  
Dynamic Performances ◽  
A New Technique

Control of Induction Motor (IM) is well known to be difficult owing to the fact the models of IM are highly nonlinear and time variant. In this paper, to achieve accurate control performance of rotor position control of IM, a new method is proposed by using adaptive inverse control (AIC) technique. In recent years, AIC is a very vivid field because of its advantages. It is quite different from the traditional control. AIC is actually an open loop control scheme and so in the AIC the instability problem cased by feedback control is avoided and the better dynamic performances can also be achieved. The model of IM is identified using adaptive filter as well as the inverse model of the IM, which was used as a controller. The significant of using the inverse of the IM dynamic as a controller is to makes the IM output response to converge to the reference input signal. To validate the performances of the proposed new control scheme, we provided a series of simulation results.

The Study of Parameter Robustness of Induction Motor Variable Frequency Speed Regulation Adaptive Inverse Control System

2013 ◽  
Vol 313-314 ◽  
pp. 3-6
Author(s):  
Guang Rui Zhang ◽  
Jian Xue
Keyword(s):  
Control System ◽  
Induction Motor ◽  
Regulation System ◽  
Variable Frequency ◽  
Time Varying ◽  
Motor Speed ◽  
Adaptive Inverse Control ◽  
Inverse Control ◽  
Speed Regulation ◽  
Parameter Robustness

Aiming at the problem that the existence of the time varying parameter in the induction motor variable frequency speed regulation system influence the induction motor speed control performances through influences on the vector control decoupling performances, the author puts forward the solution that is to apply the adaptive inverse control scheme to the induction motor speed control system. Research findings suggest that adaptive inverse control is of high parameter robustness to both gradient parameter perturbation and mutational parameter robustness, which effectively reduces induction motor time varying parameters influences on dynamic and static performances of the frequency speed regulation system.

scholarly journals A New Solution to Adaptive Inverse Control

1991 ◽  
Vol 24 (1) ◽  
pp. 35-40
Author(s):  
A. Farhang-Boroujeny ◽  
K. Ayatollahi
Keyword(s):  
Adaptive Inverse Control ◽  
Inverse Control

Research of the 3-DOF Helicopter System Based on Adaptive Inverse Control

2013 ◽  
Vol 389 ◽  
pp. 623-631 ◽  
Author(s):  
Xiu Yan Wang ◽  
Ying Wang ◽  
Zong Shuai Li
Keyword(s):  
Neural Network ◽  
Flight Control ◽  
Fuzzy Inference ◽  
Reference Model ◽  
Back Propagation ◽  
Back Propagation Algorithm ◽  
Actual System ◽  
Adaptive Inverse Control ◽  
Inverse Control ◽  
Network Connection

For the flight control problem occurred in 3-DOF Helicopter System, reference adaptive inverse control scheme based on Fuzzy Neural Network model is designed. Firstly, fuzzy inference process of identifier and controller is achieved by using the network structure. Meanwhile, the neural network connection weights are used to express parameters of fuzzy inference. Then, back-propagation algorithm is adopted to amend the network connection weights in order to automatically identify the fuzzy model and adjust its membership functions and parameters, so that the actual system output of adaptive inverse controller control which is adjusted can track the reference model output. Finally, the simulation result of 3-DOF Helicopter System based on the scheme shows that the method is effective and feasible.

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