The inherent high nonlinearity of Switched Reluctance Machines (SRMs) poses significant challenges to modeling. Conventionally, large-data-size lookup tables (LUTs) are used, which consume extensive storage space in digital signal processors (DSPs) and complicate the application of well-established PI control techniques. This paper presents a linear flux SRM model, leveraging the space mapping method to map the SRM's nonlinear flux profile into a linear space in relation to both current and position. The proposed linear model offers two key advantages. First, it reduces data storage needs due to smaller LUTs. Second, it enhances the feasibility of applying PI control. Simulation results of an 8/6 SRM are presented to illustrate the proposed approach.

The vibration/noise and torque ripple are two inherent issues for switched reluctance motors (SRMs), one of the effective methods to address these two issues is flattening both the total radial force and total torque. In this paper, an optimized quadratic-sharing-function-based radial force and torque simultaneous flattening method is proposed for SRMs. Firstly, the features of the radial force and torque characteristics are analyzed without considering saturation. Then, with these unique and specific features, four regions are defined to develop the simultaneous radial force and torque control method based on the linear sharing functions. To incorporate the saturation effects in the proposed method, the quadratic sharing functions are adopted. The proper parameters of the quadratic sharing functions and the region definition at different operating conditions are obtained through the genetic algorithm (GA) optimization. The switching angles and reference current of the conventional current chopping control (CCC) method are also optimized by GA for fair comparison purpose. Extensive simulation results are presented in this paper, and these results prove the effectiveness and superiority of the proposed method regarding the simultaneous radial force and torque control.

Proper current control is essential in SRM drives in order to ensure adequate reference tracking and torque ripple reduction when using current profiling techniques. In this context, this paper proposes a PWM super-twisting sliding mode current controller for switched reluctance motors with a fixed-gain structure. The approach presents a model-free structure, not requiring model information on implementation. The PWM implementation ensures a fixed switching frequency and the fixed-gain approach leads to a simple control structure, not demanding any sort of gain lookup tables. The complex task of gain design for super-twisting controllers is solved in this paper by means of an optimization-based design methodology, using the sum of the squared error as a cost function. The proposed technique is compared to a higher sampling hysteresis controller in terms of current and torque root-mean-square error. Simulation results are presented, showing that the proposal achieves a performance comparable to a higher sampling hysteresis controller.