# Predictive Current Control for Switched Reluctance Motor Based on Local Linear Phase Voltage Model
## 1 Introduction
In the past decade, model predictive control (MPC) has been used in controlling the current of SRM owing to its advantages of accurate tracking ability and fast response. In [20; 21], two kinds of single-step MPC strategies, also known as deadbeat predictive control (DBPC) strategies, were proposed. One was based on offline flux linkage characteristics, while the other depended on offline incremental inductance and online estimation of back EMF. With the knowledge of the magnetic characteristics, the required phase voltage in the next digital time step for eliminating the current error can be directly calculated according to the phase voltage equation. These methods have the merit of a fixed switching frequency. Nevertheless, they all need additional memory to store the lookup tables to obtain flux linkage and incremental inductance and are susceptible to the error between the stored characteristics and the actual ones due to motor aging and uncertainties in mass production [22]. In [23; 24], the current change rate of one phase in the \(j\)th (\(j=1,2,\dots\) ) switching cycle from the start of the phase conduction was assumed to be the same as those of the other two phases in each electrical period. Under this assumption, the current change rate and hence the PWM duty cycles of the conducting phase could be predicted based on the logged data regarding the two previously conducted phases. This DBPC method has the advantages of model independence as well as accurate and fast-tracking ability at high speed. However, this method also requires additional memory to record historical data. At low speed, the required memory capability would be very large due to a large number of switching cycles and historical data in each electrical period. In addition, this assumption is too rigorous to meet the requirements for actual low-speed operation. The above DBPC schemes all have the drawback of vulnerability to measurement noise due to their large gain. In [25], an extended state observer was proposed to mitigate the effect of disturbances on DBPC. However, the setting of the observer parameters is not yet clear. In [26; 27], stochastic MPC was applied for current tracking in an SRM drive by calculating the optimal duty cycles based on the inductance model, which was dynamically calibrated to suppress the effect of the measurement noise and model uncertainties. In [28],the authors applied a fixed-switching-frequency MPC method based on a modified power converter with only one current sensor to reduce the cost and volume of the SRM drive. A predictive current control (PCC) for SRM drives is proposed in this paper in order to realize fast and accurate current tracking. The relationship between the phase voltage and current slope in a sufficiently short period is described by a linear model, which is identified online using the historical data of the average voltage and current slope during the last control cycle. The required average voltage during the next PWM cycle for eliminating the current error at the end of the cycle is then predicted based on the model. The practicability of the controller was examined experimentally. The results show that the proposed controller can significantly reduce the current and torque ripples compared with the hysteresis controller. The main contributions of this paper can be summarized as follows:  
1. A local linear phase voltage model that describes the relationship between the phase voltage and the phase current slope in a short period;
2. Predictive current control combined with the voltage model identification online. Compared to the DBPC control mentioned above, the proposed PCC does not require a priori knowledge of the motor parameters and is insensitive to the parameter variation. Compared to HCC, the proposed PCC can greatly reduce the current and torque ripples in a wide speed range without increasing the current sampling rate. The rest of this paper is organized as follows: Section 2 describes the proposed PCC in detail.