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首页> 外文期刊>International Journal of Innovative Computing Information and Control >ROBUST SPEED CONTROL OF BRUSHLESS DC MOTOR BASED ON ADAPTIVE NEURO FUZZY INFERENCE SYSTEM FOR ELECTRIC MOTORCYCLE APPLICATION
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ROBUST SPEED CONTROL OF BRUSHLESS DC MOTOR BASED ON ADAPTIVE NEURO FUZZY INFERENCE SYSTEM FOR ELECTRIC MOTORCYCLE APPLICATION

机译：基于自适应神经网络模糊推理系统的无刷直流电动机的鲁棒速度控制

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摘要

Electric vehicles have been widely discussed in some articles since the cost of fuel for conventional vehicles in this era is not stable and tends to increase. And also, conventional vehicles are also not fully eco-friendly and have poor efficiency. Electric vehicles, mostly, use Brushless Direct Current (BLDC) motor as the prime mover, since it has a simple structure, good performance and high efficiency. This paper presents an Adaptive Neuro Fuzzy Inference System (ANFIS) controller to control the speed of BLDC motor applied for electric motorcycle. ANFIS controller was designed and evaluated, then compared to Proportional-Integral-Derivative (PID) and Fuzzy-PID controllers. ANFIS is trained based on the data of Fuzzy-PID performances with slight modification. According to the study, ANFIS controller has better performances compared to PID and Fuzzy-PID controllers with average steady state error of 0.13% when the speed reference changes and 0.16% when the load changes. Moreover, ANFIS controller obtains 0.27 s for rise time according to 3000 rpm of speed reference, while the other controllers have longer time to reach the speed reference.
机译：电动汽车已在某些文章中得到了广泛讨论，因为在这个时代，传统汽车的燃料成本不稳定并且趋于增加。而且，常规车辆也不是完全环保，效率也很差。电动汽车大多使用无刷直流（BLDC）电动机作为原动机，因为它具有结构简单，性能好和效率高的特点。本文提出了一种自适应神经模糊推理系统（ANFIS）控制器，用于控制应用于电动摩托车的BLDC电动机的速度。设计并评估了ANFIS控制器，然后将其与比例积分微分（PID）和Fuzzy-PID控制器进行了比较。 ANFIS是基于Fuzzy-PID性能数据进行了稍微的修改而训练的。根据研究，与PID和Fuzzy-PID控制器相比，ANFIS控制器具有更好的性能，当速度给定值变化时，平均稳态误差为0.13％，而当负载变化时，平均稳态误差为0.16％。此外，ANFIS控制器根据3000 rpm的速度参考值获得0.27 s的上升时间，而其他控制器需要更长的时间才能达到速度参考值。

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