RETRACTED: An efficient grasshopper optimization with recurrent neural network controller-based induction motor to replace flywheel of the process machine

Abstract

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.

Design of English Translation Model Based on Recurrent Neural Network

“…Li and others put forward machine translation models based on statistics and examples, which are based on large-scale real text processing and use corpus processing means to transform corpus into language knowledge base. is is an important revolution in the history of machine translation and also pushes natural language processing to a new stage of rapid development [5]. Aziz and others believe that the basis of language is rules, and the sentences of a language can be derived from its grammar, sentence pattern, and other rules.…”

Flywheel energy storage systems: A critical review on technologies, applications, and future prospects

“…142 For eliminating non-linearity of the FESS unit, a feedback linearization (FL) technique has been implemented in the literature. 143 Field oriented control (FOC) and direct torque control (DTC) are employed to regulate the speed of the FESS. 144 The model predictive control (MPC) method is more efficient than the PI controller as it procures better accuracy in response to flywheel speed control and regulation of DC-link voltage.…”

“…Control strategies based on fuzzy logic control (FLC) have been proposed by many researchers to mitigate power quality issues and enhance the quality of power being fed to the power grid 117 . The charging and discharging operation of FESS has been reported to be controlled by artificial neural network (ANN) 142 . For eliminating non‐linearity of the FESS unit, a feedback linearization (FL) technique has been implemented in the literature 143 .…”

Combined adaptive neural network and regressor‐based trajectory tracking control of flexible joint robots

“…The weights of neural networks can be computed online, or can be computed offline with different training algorithms. In the literature, works where offline algorithms are employed to control complex systems have been given in [47][48][49].…”