Department of Electrical Engineering, University of Engineering and Technology, Lahore, Punjab, Pakistan.
Department of Electrical Engineering, University of Botswana, Gaborone, Botswana.
PLoS One. 2024 Oct 31;19(10):e0310080. doi: 10.1371/journal.pone.0310080. eCollection 2024.
A brushless DC (BLDC) motor is likewise called an electrically commutated motor; because of its long help life, high productivity, smaller size, and higher power output, it has numerous modern applications. These motors require precise rotor orientation for longevity, as they utilize a magnet at the shaft end, detected by sensors to maintain speed control for stability. In modern apparatuses, the corresponding, primary, and subsidiary (proportional-integral) regulator is broadly utilized in controlling the speed of modern machines; however, an ideal and effective controlling strategy is constantly invited. BLDC motor is a complex system having nonlinearity in its dynamic responses which makes primary controllers in efficient. Therefore, this paper implements metaheuristic optimization techniques such as Whale Optimization Algorithm (WOA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Accelerated Particle Swarm Optimization (APSO), Levy Flight Trajectory-Based Whale Optimization Algorithm (LFWOA); moreover, a chaotic map and weight factor are also being applied to modify LFWOA (i.e., CMLFWOA) for optimizing the PI controller to control the speed of BLDC motor. Model of the brushless DC motor using a sensorless control strategy incorporated metaheuristic algorithms is simulated on MATLAB (Matrix Laboratory)/Simulink. The Integral Square Error (ISE) criteria is used to determine the efficiency of the algorithms-based controller. In the latter part of this article after implementing these mentioned techniques a comparative analysis of their results is presented through statistical tests using SPSS (Statistical Package for Social Sciences) software. The results of statistical and analytical tests show the significant supremacy of WOA on others.
无刷直流（BLDC）电机也称为电子换向电机；由于其寿命长、生产效率高、体积小、功率输出高，因此在现代应用中具有众多优势。这些电机需要精确的转子定位来延长使用寿命，因为它们在轴端使用磁铁，通过传感器检测来保持速度控制的稳定性。在现代设备中，相应的、主要的和辅助的（比例积分）调节器广泛用于控制现代机器的速度；然而，理想和有效的控制策略始终是受欢迎的。BLDC 电机是一个复杂的系统，其动态响应具有非线性，这使得主控制器效率低下。因此，本文采用鲸鱼优化算法（WOA）、粒子群优化算法（PSO）、蚁群优化算法（ACO）、加速粒子群优化算法（APSO）、基于莱维飞行轨迹的鲸鱼优化算法（LFWOA）等元启发式优化技术；此外，还应用混沌映射和权重因子来修改 LFWOA（即 CMLFWOA），以优化 PI 控制器来控制 BLDC 电机的速度。使用无传感器控制策略的无刷直流电机模型结合元启发式算法在 MATLAB（矩阵实验室）/Simulink 上进行了仿真。采用积分平方误差（ISE）准则来确定基于算法的控制器的效率。在本文的后半部分，在实施了这些技术之后，通过使用 SPSS（社会科学统计软件包）软件进行统计测试，对它们的结果进行了比较分析。统计和分析测试的结果表明，WOA 在其他算法中具有显著的优势。
