Linear induction motor control using sliding mode considering the end effects

2009 ◽  
Author(s):  
A. Boucheta ◽  
I. K. Bousserhane ◽  
A. Hazzab ◽  
B. Mazari ◽  
M. K. Fellah
Keyword(s):  
Motor Control ◽  
Induction Motor ◽  
Sliding Mode ◽  
End Effects ◽  
Linear Induction ◽  
Linear Induction Motor

scholarly journals Adaptive Command-Filtered Fuzzy Nonsingular Terminal Sliding Mode Backstepping Control for Linear Induction Motor

2020 ◽  
Vol 10 (21) ◽  
pp. 7405
Author(s):  
Li Zhang ◽  
Yan Xia ◽  
Weiming Zhang ◽  
Weilin Yang ◽  
Dezhi Xu
Keyword(s):  
Induction Motor ◽  
Control Method ◽  
Controller Design ◽  
Sliding Mode ◽  
Fuzzy Logic Systems ◽  
End Effects ◽  
Terminal Sliding Mode ◽  
Linear Induction ◽  
Linear Induction Motor ◽  
Nonsingular Terminal Sliding Mode

This paper puts forward a projection-based adaptive command filtered fuzzy nonsingular terminal sliding mode backstepping (PACFTB) control method for the speed control of the linear induction motor (LIM) with unknown end effects. Firstly, the technique of fuzzy logic systems (FLS) is investigated to approximate the nonlinear components of the LIM’s mathematical model, which reduces the difficulty and cost of controller design. Then, a constrained command-filtered backstepping controller is designed with a filtering compensator compensating for the inherent error of constrained filter. Moreover, the nonsingular terminal sliding mode control method is combined in the controller design for its advantages of finite-time convergence of the system, and the projection-operator-based adaptive laws are established at the same time. Finally, the stability analysis proves that the boundedness and stability of all signals can be ensured with the proposed PACFTB controller, and the simulation results along with experiment results verify that the proposed control strategy has better control performance than the conventional command filter backstepping and PI controller.

Field-Circuit Coupled Modeling for Linear Induction Motor Drive System

2013 ◽  
Vol 416-417 ◽  
pp. 711-717
Author(s):  
Zhi Hua Zhang ◽  
Li Ming Shi ◽  
Hua Cai ◽  
Yao Hua Li
Keyword(s):  
Mathematical Model ◽  
Induction Motor ◽  
Circuit Simulation ◽  
Power Converter ◽  
Drive System ◽  
Coupled Modeling ◽  
End Effects ◽  
Linear Induction ◽  
Linear Induction Motor ◽  
Motor Drive System

Linear drive system is widely applied in mid-low speed Maglev, subway transportation, etc. It is composed of two principal components, high power converter and linear induction motor. The converter and motor are designed separately, whole drive system usually use circuit simulation by extracting the mathematical model of linear induction motor. However, LIM has complex electromagnetic field, which needs to considerate the transverse and longitude end effects [1-. This makes LIM mathematical model inaccurate, hard to simulate the real dynamic characteristics of LIM.

scholarly journals Motion control of linear induction motor based on optimal recurrent wavelet neural network-PID controller

2018 ◽  
Vol 7 (4) ◽  
pp. 2028 ◽  
Author(s):  
Ameer L. Saleh ◽  
Badiryah A. Obaid ◽  
Adel A. Obed
Keyword(s):  
Neural Network ◽  
Induction Motor ◽  
Pid Controller ◽  
Pulse Width Modulation ◽  
Pso Algorithm ◽  
Wavelet Neural Network ◽  
Voltage Source ◽  
End Effects ◽  
Linear Induction ◽  
Linear Induction Motor

This paper contains a proposed controller based on optimal recurrent wavelet neural network (RWNN) with PID controller to control the velocity and hence the stator current as well as the developed thrust of three phase linear induction motor (LIM) which consider the end effects. A vector control represented by indirect field oriented control (IFOC) technique is appointed to attain velocity and flux control for different loading conditions. Moreover, a voltage source inverter based on space vector pulse width modulation (SVPWM) is utilized to give the required stator voltage of LIM. A well-known particle swarm optimization (PSO) algorithm is employed for online tuning of the proposed controller. The computer simulation results show that this controller is effective and gives preferable and rigorous performance compared with a results obtained from conventional wavelet neural network (WNN) and PID controllers.  

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