ESTIMATION OF DC SERVOMOTOR PARAMETERS USINGGREY WOLF, WHALE AND GENETIC ALGORITHM OPTIMISATION TECHNIQUES

Abstract

This paper investigates the use of time-domain response with recent meta-heuristic optimisation techniques such as; Grey wolf optimisation, Whale optimisation algorithm, and Genetic algorithm for the estimation of the parameters of a servomechanism with insufficient datasheet information. First, to estimate the servomechanism’s closed-loop position second-order transfer function, the known servo gear mechanism and unknown speed first-order transfer functions were used. In the experimental investigation, the average time constant of the Servomechanism was found to be 162mS for both forward and reverse rotations. Thereafter, the meta-heuristic algorithms were used in MATLAB to simulate the identified position closed-loop velocity feedback transfer function to obtain the Servomechanism’s electromechanical parameters. The simulation and experimental response of the servomechanism were in excellent agreement, with the Genetic and Whale optimisation algorithms having the best and worst root mean squared error fitness scores of 0.00706 and 1.90374 respectively.