Pareto-optimal firing angles for switched reluctance motor control

Research into integrated control of the severely nonlinear switched reluctance motor is in its infancy. This paper reports an application of genetic algorithms to this area, aiming at providing motor and drive engineers with a helpful method and data for commissioning. Using the genetic algorithm method, optimal firing angles are obtained for maximal torque control under multiple operating conditions. Fur 'minimum commitment design' at the CAD stage, Pareto-optimal firing angles are also evolved for both efficiency and torque maximisation, which have not been successful in the past due to methodological limitations. The outcome should be of immediate use in inverse model based optimal operation and integrated manufacturing of switched reluctance motors.