# An NUTSF at Sub-Region for Suppression of Torque Ripple in Switched Reluctance Motors
## 1 Introduction  
The switched reluctance motor (SRM) has a unique doubly salient structure and, no winding on rotor, and has a low cost and durability, which makes it a good prospect for application in electric vehicles and the aerospace field [1, 2]. However, an SRM has a higher torque ripple, vibration and noise compared with other motors. The high torque ripple can be further reduced by optimizing the SRM's structure and improving the control strategy [3, 4]. A vibration prediction method based on multi-physical analysis was proposed, which has a high prediction accuracy for SRMs [5]. According to the nonlinear characteristic of SRM, the turn-on angle and turn-off angle are designed, in [6, 7], to reduce the torque ripple. In [8], the influence of the turn-on and turn-off angles on the current harmonic was analyzed, and the law between the vibration and conduct angles was obtained; it was concluded that the order of the harmonic is related to speed and load. The literature [9] shows that the vibration and noise can be suppressed by eliminating the third harmonic in the radial force of an SRM based on the relationship between the radial force and vibration. The noise of an SRM can be reduced by improving the harmonic current, as shown in [10]. A new direct torque control method was proposed, where a controller based on the Lyapunov function was obtained, which effectively reduces the torque ripple [11]. In [12], a fault-tolerant power converter scheme based on the entropy theory was presented, which can identify the faulty leg and the type of power semiconductor fault. The torque sharing function, taken as an effective method to suppress the torque ripple, has been widely researched and applied in recent years. The four conventional TSFs have been evaluated and optimized [13]. A TSF control strategy based on a nonlinear formula was proposed in [14], which improves the tracking performance of the current. In [15], a new family of TSFs were proposed by optimizing the peak value of the currentand the maximum absolute value of the change rate of flux linkage, which improves the efficiency of an SRM. The literature [16] takes into consideration three objects altogether in a family of TSFs: the efficiency, torque ripple and peak current, which expand the range of speeds. An offline TSF was presented in [17] where the offline TSF was selected by balancing the wide speed performance and efficiency, and the copper consumption of the SRM was reduced. A compensation strategy based on the TSF was designed in [18] where the torque error was compensated by a proportional-integral controller. The error between the torque reference and the actual torque was compensated in order to improve the torque tracking performance in the TSF [19]. The literature [20; 21; 22; 23] applied the model to predict the current and torque, but it took a lot of memory. The improved TSF was designed offline by choosing the angle of the rotor position, and a neural network model was built [24]. In [25], the relationship between copper loss and the torque ripple was balanced by optimizing the offline TSF parameter. The literature [26; 27] showed how to control the SRM by using angle position control (APC). The current chopping control (CCC) method is commonly used in SRM drive control because of its simple calculation and good reliability, but the larger torque ripple limits its further application [28; 29]. The many optimizing liaison (MOL) algorithm and gravitational search algorithm (GSA) were used to optimize the parameters of PI, and the turn-on and turn-off angles, respectively, in [30], and this showed that the MOL algorithm is superior to the GSA. In [31], the Q learning algorithm was applied to the current tracking control, which effectively improves the current tracking capability. A non-unity torque sharing function at each sub-region is proposed based on inductance characteristics in order to reduce the torque ripple of an SRM.