Systems identification of servomechanism parameters using jellyfish, particle swarm and constraint optimization

Abstract

In this paper, DC servomechanism parameters were identified offline using Jellyfish, particle swarm and constraint optimization techniques in a MATLAB simulation environment with experimental data. Specifically, the unknown parameters of the servomechanism were identified using a two-step approach. Initially, the first-order transfer function of the servomechanism which is characterized by a DC gain and time constant was determined analytically using the experimental open-loop speed step response of the servo motor. Next, by iterative minimization of a fitness score derived from the root mean squared error between the experimental and simulated position response of the servomechanism of an equivalent state-space model structure, the servomechanism parameters were identified. The simulated angular position step response of the servomechanism with the particle swarm, Jellyfish and constraint optimization algorithm, showed excellent agreement with the experimental data in descending order and was consistent with the fitness score of 1.9035, 0.0083, and 0.00706 respectively.

Recent advances in use of bio-inspired jellyfish search algorithm for solving optimization problems

“…Nyong-Bassey and Epemu 112 used JSO and PSO to identify servomechanism parameters using a two-step approach, involving a first-order transfer function and iterative minimization of a fitness score that is derived from the root mean squared error between the experimental and simulated position responses of the servomechanism of an equivalent state-space model structure. The simulated angular position step responses of the servomechanism that runs the JSO and PSO algorithms showed very closely with each other, in terms of root mean squared error.…”