This paper proposes a convenient power electronic circuitry with a control approach for the flywheel replacement of an induction motor. The proposed control approach is the joined execution of grasshopper optimization algorithm and recurrent neural network based on duty ratio controller and hence the proposed work is named grasshopper optimization with recurrent neural network. The main contribution of this work is, the power electronic circuitry gets the input voltage samples and limits the deviation to appraise the instantaneous torque demand. The required voltage for the instantaneous torque demand is produced by the proposed control technique. In the proposed grasshopper optimization with recurrent neural network technique, the grasshopper optimization algorithm is a meta-heuristic population-based algorithm, which works from the perspective of the swarming behavior of grasshoppers in nature. In the proposed system, the recurrent neural network learning procedure is improved by the grasshopper optimization algorithm in the perspective of the minimum error objective function. the proposed grasshopper optimization with recurrent neural network technique optimizes the inverter switching states by limiting the error between the setpoint torque and the demand torque regarding objective function. With this proposed technique, the unbalance between demand torque and generated torque is found with high precision and the quicker execution to pull back out the torsional pulsation insensitive load linked transmission systems. By utilizing the proposed methodology, the extreme fluctuation of load torque due to peaky loads in an induction motor will be detected accurately. Also, the proposed technique reduces the torsional vibrations, weakness in components and minimizes the outages of uninterrupted production leading to higher profits. The proposed strategy is actualized in the MATLAB/Simulink platform and evaluated their performance. The performances are appeared differently compared with the existing methods.

中文翻译：

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用基于递归神经网络控制器的感应电机代替加工机飞轮的高效蚱蜢优化

本文提出了一种带有控制方法的方便的电力电子电路，用于更换感应电机的飞轮。所提出的控制方法是基于占空比控制器的蚱蜢优化算法和递归神经网络的联合执行，因此所提出的工作被命名为蚱蜢优化与递归神经网络。这项工作的主要贡献是，电力电子电路获取输入电压样本并限制偏差以评估瞬时扭矩需求。瞬时扭矩需求所需的电压由所提出的控制技术产生。在提出的采用循环神经网络技术的蚱蜢优化中，蚱蜢优化算法是一种基于元启发式种群的算法，这是从自然界中蚱蜢的蜂群行为的角度出发的。在所提出的系统中，从最小误差目标函数的角度，通过蚱蜢优化算法改进了循环神经网络学习过程。所提出的具有循环神经网络技术的蚱蜢优化通过限制设定点扭矩与目标函数需求扭矩之间的误差来优化逆变器开关状态。使用这种建议的技术，可以高精度地发现需求扭矩和产生的扭矩之间的不平衡，并且可以更快地执行以拉回对扭转脉动不敏感的负载链接传输系统。通过利用所提出的方法，准确检测感应电机峰值负载引起的负载转矩的极端波动。此外，所提出的技术减少了扭转振动、组件的弱点，并最大限度地减少了不间断生产的中断，从而提高了利润。所提出的策略在 MATLAB/Simulink 平台上实现并评估其性能。与现有方法相比，性能出现不同。