Switched reluctance machines (SRMs) are gaining increasing interest in industrial applications due to their low manufacturing cost, simple and robust structure, excellent fault-tolerant capability, and reliable operation in high-temperature operating environments. However, the inherent pulsative torque and radial force lead to the well-known torque ripples and acoustic noise issues. Although there are numerous advanced methods to address the above two issues, the high nonlinearity inevitably brings difficulties in controlling the SRMs.

Since the linear SRM voltage and toque equations are simple, it would be beneficial to explore the control algorithm by using such simple linear model. The application of the linear torque model is firstly explored. To utilize such simple model, the connections between the linear toque model and the nonlinear torque model are built through the mapping. The features of these mapping curves are studied in detail. Applying the linear torque equation to generate the reference currents in the optimization-based torque sharing function method shows a significant reduction of the time consumption in solving the bi-optimization problem.

Later, the complete equivalent linear SRM model is constructed by introducing the linear voltage equation and corresponding mapping. Since the linear model is easy to predict the behaviour of SRMs, it is beneficial to apply such model in the model predictive torque control (MPTC) methods. The application of the equivalent linear model in the finite control set (FCS) MPTC method shows a low computational burden and occupies less storage space. Besides, the improved switching table in the proposed FCS MPTC method also enhances the torque control performance in high-speed operation conditions. To further reduce the torque ripples, the continuous control set (CCS) MPTC method is developed based on the constructed equivalent linear SRM model. The impossibility in analytically solving the optimization problem in the CCS MPTC method if using the original nonlinear SRM model is innovatively addressed by using the equivalent linear SRM model and properly modifying the cost function. Extensive simulation and experimental results prove the low-ripple feature of the proposed CCS MPTC method in a wide speed range.

The high nonlinearity also makes the current control of SRM drives difficult. An intersection-method-based current controller is presented to ensure good current tracking performance for SRMs. The employed adaptive flux-linkage observer makes this current controller show robust performance when there is a deviation on the employed flux-linkage characteristics.

Finally, the key but unmeasurable radial force information for the advanced acoustic reduction method is reconstructed based on the measured flux-linkage curves and some core relationship. This core relationship, which is between the square root of the radial force and the flux-linkage, is explored in detail. Simulation results reveal that the proposed method shows good radial force estimation accuracy when there is even 50% airgap length variation.