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.