Abstract
In this paper a model fuzzy predictive force control (FPFC) for the speed sensorless control of a single-side linear induction motor (SLIM) is proposed. The main purpose of of predictive control is minimizing the difference between the future output and reference values. This control method has a lower force ripple and a higher convergence speed in comparison to conventional predictive force control (CPFC). In this paper, CPFC and FPFC are applied to a linear induction motor and their results are compared. The results show that this control method has better performance in comparison to the conventional predictive control method.
Highlights
Linear induction motors (LIMs) have several advantages such as the lack of need of interface mechanical tools, low mechanical losses, high starting force, and simple and strong structure
The flexibility of Model Predictive Force Control (MPFC) allows the control to include non-linear factors and to apply the limitations of the control variables
In MPFC, a cost function is usually defined based on the errors of torque amplitude and flux, but in order to have a satisfactory operation, there is a need for an appropriate weight function
Summary
Linear induction motors (LIMs) have several advantages such as the lack of need of interface mechanical tools, low mechanical losses, high starting force, and simple and strong structure. These motors are widely used in automation systems and industrial applications such as transportation systems, conveyor drives, electromagnetic launchers, and the transfer of containers in container terminals. Among the different control methods, Model Predictive Force Control (MPFC) has received increased attention as an effective method [11, 12]. In MPFC, a cost function is usually defined based on the errors of torque amplitude and flux, but in order to have a satisfactory operation, there is a need for an appropriate weight function. In [18], minimizing principles of the torque ripple for the calculation of the optimum weight coefficient have been introduced, but the equation of the optimum weight coefficient is complicated and the parameters depend on each other
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