Search

In this article, a low-ripple continuous control set (CCS) model predictive torque control (MPTC) method for switched reluctance machines (SRMs) is proposed. The inherent high nonlinearity of the SRMs makes it difficult to solve the optimization problem in the CCS MPTC algorithm analytically. To address this issue, an equivalent linear SRM model is adopted, and the cost function is also properly modified. Then, with the torque boundary values provided by executing the voltage vectors of an improved switching table, the optimization problem in the CCS MPTC method becomes simple and analytically solvable. The Lagrange multiplier method is employed to solve this optimization problem analytically and generate the optimum torque reference values for the active phases. Based on the estimated torque variation rates, the duty cycles for each phase are calculated. Extensive simulation and experimental tests are carried out in a four-phase 8/6 SRM setup. These testing results reveal that the proposed CCS MPTC method shows much lower torque ripples and current ripples in a wide speed range with a low computational burden than the existing finite control set MPTC methods. [ABSTRACT FROM AUTHOR]

In this article, a novel model predictive torque control (MPTC) method for switched reluctance machines (SRMs) is proposed based on the equivalent linear SRM model and the improved switching table. First, an improved switching table with only six switching states is developed based on the inductance characteristics. The adoption of this improved switching table not only reduces the computational burden by 25% but also improves the torque control performance and system efficiency at high-speed region. However, the look-up-table (LUT) based MPTC methods suffer from occupying numerous storage space. To ease this issue, the simple linear SRM model is utilized. The flux-linkage and torque equivalent transformations are introduced to address the difference between the linear and nonlinear SRM models. With these transformations, the MPTC realized on the linear SRM model is proposed. Compared to 1530 storage units consumption to store three two-dimensional (2-D) LUTs in the LUT-based MPTC methods, the proposed method only occupies 228 storage units. Experimental results on an 8/6 SRM setup verify that the proposed method effectively eliminates the commutation torque ripple with lower execution time, less storage space, and improves the torque control performance at a high-speed range compared with the conventional LUT-based method. [ABSTRACT FROM AUTHOR]

In this article, a time-efficient torque shaping technique for the switched reluctance machines is proposed to reduce the torque ripples while optimizing the copper loss. To reduce the computational time for solving the time-consuming bi-objective optimization problem, an efficient and desirable linear space for the SRM is utilized through the torque mapping between the linear space and nonlinear space. The feasible regions and properties of this torque mapping for SRM are studied in detail first. Then, based on this torque mapping, the current reference is generated by solving a convex optimization problem in the linear space and later converted to the current reference in nonlinear space. Compared to the traditional method that only adopts nonlinear space, the proposed current reference generation method requires less than 10% of the time. Furthermore, the proposed method saves more than 70% time consumed by the traditional method for the bi-objective optimization problem but possesses the similar torque ripple reduction performance. The effectiveness of the proposed method has been verified by extensive simulation and experimental tests on a four-phase 5.1 kW 8/6 SRM setup. [ABSTRACT FROM AUTHOR]

Relatively high torque ripple limits the further development of the switched reluctance machine, and the application of the ripple suppression method will reduce the efficiency of the system. To simultaneously improve the efficiency and torque ripple of the switched reluctance machine, a new commutation and online optimization strategy is proposed. According to the torque per ampere characteristics of the machine, two rotor position angles are defined, which are the boundaries for the high and low torque per ampere operation modes of the two adjacent phases, respectively, and for different operating modes, specific torque hysteresis control schemes are performed. Furthermore, an objective function is constructed with the efficiency and torque ripple coefficient, and during the operation of the machine, the defined boundary angles are online optimized by the genetic algorithm to maximize the value of the objective function. The effectiveness of the method is verified by detailed simulation and experiment under different operating conditions. [ABSTRACT FROM AUTHOR]