# Predictive Current Control for Switched Reluctance Motor Based on Local Linear Phase Voltage Model
## 1 Introduction  
The switched reluctance motor (SRM) has attracted increasing attention because of its simple and robust structure, absence of permanent magnets and windings in the rotor and high efficiency in a wide speed range. However, SRM has the main disadvantage of high torque ripple due to its double-salient structure [1; 2; 3]. In order to reduce the torque ripple, numerous solutions related to motor design or motor control have been proposed. Torque control methods can be divided into direct torque control and indirect torque control (ITC). In ITC, the torque is controlled by indirectly controlling the stator phase currents [4; 5; 6]. Among ITC methods, the torque sharing function (TSF) is commonly used, which distributes the total torque reference into two phase torque references in the commutation region. Based on the nonlinear phase torque characteristics, the phase torque references are converted to phase current references [7; 8]. The final torque ripple suppression effect is highly dependent on the tracking performance of the phase current reference. Hysteresis current control (HCC) is the most widely adopted method for controlling phase currents in SRM owing to its distinctive merits, such as simple structure, model independence, fast dynamic response, strong robustness, insensitivity to motor parameter variation and so on [9; 10; 11]. However, the switching frequency is unfixed in HCC due to the strong nonlinear magnetic characteristics of SRM, which will increase the difficulty of designing an electromagnetic interference (EMI) filter. In addition, in digital control systems,the control frequency is limited by hardware performance issues, such as the current sampling rate, processor speed and switching time of the power switches. The actual current may continuously deviate from its reference for about a complete control cycle, resulting in a large current ripple. High DC-link voltage and low incremental inductance will cause a larger current ripple. Improving the hardware performance contributes to a reduction in the current ripple, but it will increase the cost of the digital system. A PI controller is also routinely used for motor current control. A PI controller controls the current by regulating the phase voltage with PWM, which has the advantage of a fixed switching frequency. However, the PI controller suffers from the difficulty in tuning gains due to the strong nonlinearity of SRM. It is hard to find only one set of PI parameters suitable for different speeds and loads simultaneously. Therefore, the variable-gain PI current controller has received research interest. In [12], the back electromotive force (EMF) was derived online by using a two-dimensional (2D) lookup table containing the partial derivative of flux linkage with respect to the position, and its effect on current control was decoupled by feedforward control. To save memory and computational time, the PI gains were simply adapted linearly with both the position and current based on the incremental inductance. In [13], the back EMF and incremental inductance were estimated online by using a pretrained B-spline neural network. The proportional gain and integral gain were linearly tuned with the incremental inductance and winding resistance, respectively. However, training the network was very time-consuming. In [14; 15], a Fourier series was used for modeling the incremental inductance and decoupling the effect of the back EMF. In [16], phase winding was modeled by a resistance that varied with the rotor speed and the average reference current, and a pure integral controller was used for controlling the average current in a 120-electrical-degree-wide conducting region. In [17; 18], the fixed PI gains most suitable for different operating points were selected based on a piecewise linear flux linkage model. In [19], fractional-order PI control was applied, and the PI parameters were optimized using ant colony optimization.