Optimal control design for switched reluctance machines with right-sized modeling

Compared to the traditional electric machines, switched reluctance machines (SRM) exhibit significant improvement on production, robustness and efficiency. However, some inherent drawbacks of those kinds of machines such as noise and high torque ripple at low speed have to be well managed. Moreover, improved performance such as higher energy density, better dynamic response are to be expected. To solve these challenging issues, dedicated control strategies usually provide more efficient ways, in contrast to other methods like mechanical re-design. Accurate and right-sized mathematical models are crucial for the controller synthesis using iteration based multi-objective optimization approach. Due to the strong nonlinear characteristics of SRMs, deriving the right-sized models of a SRM is really a challenging work. In this paper, the analytical representation of SRM magnetic characteristics with adjustable complexity are derived and used for multi-objective optimization based controller synthesis. Furthermore, inverse modeling technique is applied to achieve the exact mapping between commanded current and torque for the close-loop current control, which can significantly improve the system performance relating to torque ripple, control accuracy, etc. The simulation results of a 22KW 16/12, 4 phase SRM is presented to illustrate the proposed design approaches.