Abstract
This paper presents a speed control of the reluctance machine for electric drive applications with fast dynamic demand. To get high-performance speed control, a cascade control algorithm is developed based on linear control technique. The controller is designed using the Root Locus Methodology and implemented on a numerical simulation platform. The design using Root Locus Methodology proved to be a viable approach and showed that various problems associated with the structural torque ripple of the electric motor can be solved. An important aspect of this work is the role played by model linearization in testing the sensitivity of the controller performance to specific parameter changes. The controller is applied to a simulated non-linear switched reluctance motor model in order to evaluate their performances. Simulation results showed that high-performance control for Switched Reluctance Motor has been achieved.

Abstract
Switched reluctance machines (SRM) are simple, robust and fault tolerant machines, usually operating under strong nonlinear characteristics. Hence, modeling this machine is a most demanding task. While magnetic saturation is often addressed, hysteresis effect is usually disregarded. In order to include this phenomenon, an SRM drive simulation model was built, where magnetization characteristics are generated through the Jiles-Atherton (J-A) hysteresis model. SRM losses estimation is a challenging task, which demands continuous research efforts. This paper intends to investigate hysteresis impact on SRM copper losses. Due to the machine features, skin and proximity effects are considered. Different steady-state operation scenarios are simulated and compared.

Abstract
Switched reluctance machines (SRM) are simple, robust and fault tolerant machines, usually operating under strong nonlinear characteristics. Most SRM models address magnetic saturation, but hysteresis effect is usually disregarded. This paper is based on a developed four-quadrant SRM drive simulation model, where magnetization characteristics are generated through the original Jiles-Atherton (J-A) hysteresis model. The main goal is to investigate the hysteresis impact on the SRM drive controller performance. Steady-state operation scenarios are simulated and compared. For the adopted current control strategy (PWM), the results show a significant impact in all drive components, particularly for low speed with high load operation.

Abstract

Abstract
Switched reluctance machines are simple, robust, fault-tolerant and do not use permanent magnets, which makes them a strung candidate fur vehicular propulsion. Despites the advantages they still stiffer from high torque pulsation and acoustic noise, which can be reduced by the controller. In this paper the concern is in having an advanced current control, so it is used the model predictive control (MPC). This requires an accurate model to estimate the future behavior of current and the back-electromotive force (emf) signal is essential. As this signal cannot be directly calculated or measured it is proposed a new algorithm to calculate its estimation in real time. The algorithm is easy to implement and the numerical results show the accuracy of the method, which permits a very low current estimation error in the MPC framework.

Abstract
A considerable amount of research within the last few decades has been focusing on controllers for switched reluctance motor drives and how they affect the torque ripple. Despite all its potentials, there are still major concerns and obstacles to overcome concerning the dependency of the magnetic characteristic of the switched reluctance motor. This work targets these concerns by proposing an initial study of the fundamentals of a drive scheme using a finite set model predictive control for a switched reluctance motor through an asymmetric bridge converter. The implementation of this scheme is the main contribution of this paper. The method uses the dynamic model of the motor to estimate the future behavior of the current for each converter state. A cost function then evaluates which switching state minimizes the current error and applies it to the motor. Some simulation results illustrate the technique. Simulation results show the good performance of the method with fast and accurate transient response. © IFIP International Federation for Information Processing 2020.