## 1 Introduction  
Recently, with the promotion of net zero/sustainable development, more and more attention has been paid to permanent materials free and wide-speed range motors, such as switched reluctance motors (SRMs) [1; 2; 3; 4]. Due to their high robustness and stability, low cost, simple structure, and wide speed range, SRMs are becoming powerful candidates for various application fields. However, due to the inherent structure, torque ripple suppression has become one of the significant topics. Aside from innovation and the optimization of the motor structure [5; 6; 7], the advancement of the control strategy is another main aspect of reducing the torque ripple [8; 9; 10]. Scholars have carried out several control strategies, i.e., torque sharing function [11; 12], direct torque control [13; 14; 15], direct instantaneous torque control (DITC) [16], and model-predictive control [17]. Among them, DITC can obtain more accurate instantaneous torque, which avoids the complexity of indirect control [18]. In previous research, a torque-sharing algorithm was introduced to minimize ohmic losses and acoustic noise based on the DITC scheme combined with pulse-width modulation and a predictive approach [16; 19]. In addition, several other technical approaches have been integrated into DITC for performance enhancements. For instance, a modular multi-level power converter [20], parameter identification without rotor locking [21], current control [22], and a different switching strategy [23] were integrated into DITC for lower torque ripple, superior efficiency, and other better control performance. Recently, in [24], an adaptive dynamic commutation strategy based on two operational modes was proposed to reduce the torque ripples, where turn-on and turn-off angles were adjusted in each electrical cycle. In the actual operation process, the system is often interfered with by the external environment, resulting in reduced control accuracy. Hence, in addition to control strategies, intelligent control algorithms have also gained rapid development. Sliding mode control (SMC) is considered to achieve superior performance in dynamic response, torque ripple suppression, anti-disturbance ability, and robustness [25; 26]. The design of reaching law is the most common improvement approach of SMC to achieve better control performance, which has been extensively used in the observer and controller [27; 28]. In [25], the torque ripple range of the proposed scheme has been reduced by 26% under 5 Nm compared with PI. Meanwhile, the transient response can be improved by 21.4% under 1000 r/min. For instance, a super-twisting SMC [29] and a novel reaching law [30] were adopted for better robustness and tracking performance. Moreover, as reported in [31], adaptive control was utilized to provide superior robustness. Terminal SMC (TSMC) with a fast response is developed to guarantee a finite convergence time [32; 33]. In [30], the torque ripple of the proposed scheme can be reduced by 12.5% and 6.9% under 8 Nm compared with PI and conventional sliding mode controllers. Meanwhile, the transient response can be improved by 56.2% and 36% in the initial stage, respectively. Due to the relatively few applications of TSMC to SRMs compared with other types of motors in the previous works, the design and improvement of the reaching laws can be integrated with TSMC to further enhance the control performance. With the rapid development of multiple control schemes, including strategies and intelligent algorithms, the option of complex control parameters is necessary for system performance [34]. Therefore, an increasing number of optimization algorithms, for instance, the climbing algorithm [35], particle swarm optimization algorithm [36], grey wolf optimization algorithm (GWOA) [37; 38], coyote optimization algorithm (COA) [39], genetic algorithm [40], and hybrid optimization algorithms [41; 42] have been investigated to tune the adjustable for better performance.