In modern era, the switched reluctance motors (SRM) are gaining attraction due to its inherent features such as robustness, low cost, simple, rugged structure, excellent fault tolerance and temperature withstanding capability. With these specialized advantages, the accurate and efficient energy analysis is still a challenging one since they operated at variable reluctance and oscillating excitation characteristics resulting in nonlinear characteristics. This paper focused on the energy improvement and performance analysis inclusive of smooth control in speed and minimized torque ripples of SRM by introducing the novel Bio- inspired methodology named Bacterial Foraging Optimization Algorithm (BFOA) for the selection of optimal parameters of the PID speed and current controllers. By minimizing the torque ripples, it increases the average torque which in turn increases the energy conversion. The performance of the SRM is measured in terms of speed, current, power and efficiency and BFOA efficiency has been verified with genetic algorithm (GA) and conventional PID controller using Euler forward approximation method. This paper discusses the modeling, controlling strategy using BFOA, GA and Euler forward approximation method and a comprehensive analysis of energy using optimized selection of controlling parameters. The performance evaluation objectives in this work, is to minimize Integral Square Error of both current and speed controller and also the reduction of torque ripples. The results obtained in this method are also compared with the Genetic Algorithm and Euler forward approximation-based controllers. The proposed algorithm employs individual and social intelligences, which in turn search the responses between the local optima along with global optimums of the problem adaptively. The outmost dynamic response increased average torque and minimized current ripple can be obtained when the parameters of PID controllers are optimized using BFOA.

在现代，开关磁阻电机 (SRM) 由于其固有的特性，如坚固耐用、成本低、结构简单、坚固耐用、出色的容错能力和耐温能力而受到越来越多的关注。凭借这些专业优势，准确高效的能量分析仍然是一项具有挑战性的工作，因为它们以可变磁阻和振荡激励特性运行，从而导致非线性特性。本文通过引入名为细菌觅食优化算法 (BFOA) 的新型仿生方法来选择 PID 速度和电流的最佳参数，重点关注能量改进和性能分析，包括 SRM 的速度平滑控制和最小转矩脉动。控制器。通过最小化转矩脉动，它增加了平均扭矩，从而增加了能量转换。SRM 的性能在速度、电流、功率和效率方面进行测量，BFOA 效率已通过遗传算法 (GA) 和使用欧拉正向逼近方法的常规 PID 控制器进行验证。本文讨论了使用 BFOA、GA 和 Euler 正向逼近方法的建模、控制策略以及使用优化选择控制参数的能量综合分析。这项工作的性能评估目标是最小化电流和速度控制器的积分平方误差，并减少转矩脉动。该方法获得的结果还与遗传算法和基于欧拉正向逼近的控制器进行了比较。所提出的算法采用个体和社会智能，这反过来自适应地搜索局部最优与问题的全局最优之间的响应。当使用BFOA优化PID控制器的参数时，可以获得最大的动态响应增加的平均转矩和最小的电流纹波。