基于改进NSGA-Ⅱ算法的开关磁阻电机再生制动优化控制方法

Regenerative Braking Optimization Control Method of Switched Reluctance Motor Based on Improved NSGA-Ⅱ Algorithm

针对采用传统优化算法优化开关磁阻电机再生制动控制参数存在制动能量回馈效率低及制动转矩脉动系数大的问题,提出一种基于渐近约束支配法则的双目标非支配排序遗传算法。首先针对传统双目标非支配排序遗传算法优化开关磁阻电机再生制动控制参数时存在易陷入局部最优解等不足,提出一种渐近约束支配法则,再将基于渐近约束支配法则的双目标非支配排序遗传算法应用于开关磁阻电机再生制动控制参数的优化,并对其效果进行了仿真验证,同时与传统双目标非支配排序遗传算法进行了对比分析,结果表明:基于渐近约束支配法则的双目标非支配排序遗传算法不仅有效解决了易陷入局部最优解的问题,而且显著提高了开关磁阻电机再生制动效率并降低了其转矩脉动系数,取得了满意的优化效果。

Aiming at the problems of low braking energy feedback efficiency and large braking torque ripple coefficient when using traditional optimization algorithm to optimize regenerative braking control parameters of switched reluctance motor,a dual-objective non-dominated sorting genetic algorithm based on progressive constraint rule is proposed. First,against the traditional dual target non dominated sorting genetic algorithm optimization of switched reluctance motor regenerative braking control parameters easily plunged into local optimal solution and other deficiencies. A gradual control rule is proposed,then based on the gradual constraint dominate the double goal of the law of non dominated sorting genetic algorithm is applied to the switched reluctance motor regenerative braking control parameter optimization. The results are verified by simulation and compared with the traditional dual-objective non-dominated sorting genetic algorithm. The results show that: The dual-objective non-dominated sorting genetic algorithm based on the progressive constraint rule not only effectively solves the problem that it is easy to fall into the local optimal solution,but also significantly improves the regenerative braking efficiency of switched reluctance motor and reduces its torque ripple coefficient,which has good application value.