A Swarm Intelligence Based Scheme for Complete and Fault-tolerant Identification of a Dynamical Fractional Order Process

TL;DR

This paper introduces a particle swarm optimization-based method for accurately identifying parameters of fractional order processes, ensuring fault tolerance and adaptability in control systems.

Contribution

It presents a novel PSO-based scheme for fractional order process identification, improving accuracy and robustness over existing methods.

Findings

High accuracy in parameter estimation even with corrupted data

Effective fault-tolerant identification scheme

Enhanced schemes further improve identification precision

Abstract

System identification refers to estimation of process parameters and is a necessity in control theory. Physical systems usually have varying parameters. For such processes, accurate identification is particularly important. Online identification schemes are also needed for designing adaptive controllers. Real processes are usually of fractional order as opposed to the ideal integral order models. In this paper, we propose a simple and elegant scheme of estimating the parameters for such a fractional order process. A population of process models is generated and updated by particle swarm optimization (PSO) technique, the fitness function being the sum of squared deviations from the actual set of observations. Results show that the proposed scheme offers a high degree of accuracy even when the observations are corrupted to a significant degree. Additional schemes to improve the accuracy still further are also proposed and analyzed.