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The Single Linear Induction Motor (SLIM) is a specialized electrical machine that produces linear motion instead of the rotary motion produced by a traditional rotary induction motor. SLIM's accurate dynamic model is required to analyze the performance of the motor under different operating conditions. Dynamic modelling of SLIM using the traditional DQ- axis equivalent circuits is difficult due to the time-varying parameters such as end effect, air gap flux, saturation, and half-filled slot. The two methods for modelling SLIMs were compared, namely the conventional method and the split method. The results of the comparison showed that both methods provided similar results, but the split method offered a more detailed analysis of the components and provided deeper insight into the behavior of the motor. The choice between the two methods depends on the specific requirements and objectives of the analysis. In this paper, the dynamic model of SLIM is modelled using conventional and split methods in MATLAB/SIMULINK. The results of the two modelling methods are compared with each other, and it is concluded that the splitting method provides better transient performance than traditional D-Q axis methods.

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Normally speed control of a Single-Sided Linear Induction Motor (SLIM) by an indirect vector control scheme is difficult because the motor's parameters are time-dependent and the performance depends on various factors such as end effect, saturation, location of primary losses, and iron losses. Traditional PI current regulators are commonly used in vector regulators, but there is a tuning problem due to the oscillation of an operating point. This problem can be overcome by substituting an adaptive neuro-fuzzy-based current controller, and this controller improves the operation of a SLIM, such as its motor speed and thrust force. In this adaptive neuro-fuzzy controller, the ID and IQ errors and the error delay are inputs, and its outputs are Vds and Vqs, respectively. It is trained based on available values. A SLIM's dynamic modelling is implemented by dividing current (I) and flux-linkages into two terms. In these two terms, one is dependent on the end effect, and the other is independent of the end effect. The function of a Voltage Source Inverter (VSI)-fed indirect vector-controlled SLIM drive is simulated in MATLAB/Simulink, and its operation under various operating conditions is studied using an adaptive neuro-fuzzy current controller. These results are compared to a traditional P-I controller. The Pulse Width Modulation (PWM) technology that is used for controlling the VSI is called Space Vector Modulation (SVM).