Abstract

The control of power converters is difficult due to their non-linear nature and, hence, the quest for smart and efficient controllers is continuous and ongoing. Fractional-order controllers have demonstrated superior performance in power electronic systems in recent years. However, it is a challenge to attain optimal parameters of the fractional-order controller for such types of systems. This article describes the optimal design of a fractional order PID (FOPID) controller for a buck converter using the cohort intelligence (CI) optimization approach. The CI is an artificial intelligence-based socio-inspired meta-heuristic algorithm, which has been inspired by the behavior of a group of candidates called a cohort. The FOPID controller parameters are designed for the minimization of various performance indices, with more emphasis on the integral squared error (ISE) performance index. The FOPID controller shows faster transient and dynamic response characteristics in comparison to the conventional PID controller. Comparison of the proposed method with different optimization techniques like the GA, PSO, ABC, and SA shows good results in lesser computational time. Hence the CI method can be effectively used for the optimal tuning of FOPID controllers, as it gives comparable results to other optimization algorithms at a much faster rate. Such controllers can be optimized for multiple objectives and used in the control of various power converters giving rise to more efficient systems catering to the Industry 4.0 standards.

Highlights

  • The objective of this paper is to study the effect of the cohort intelligence (CI) algorithm on the optimal tuning of a fractional order proportional integral derivative (PID) controller for a buck converter

  • This article discusses a novel method for the design of a fractional order PID controller for a buck converter using the cohort intelligence meta-heuristic algorithm

  • The performance of the system with CI algorithm was compared with the genetic algorithm (GA), particle swarm optimization (PSO), artificial BEE colony (ABC), and simulated annealing (SA) optimization algorithms for different cost functions

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Summary

Introduction

Power electronic DC–DC converters are very versatile and can be used for voltage regulation in a wide range of applications. Such converters work in different modes of operation, which introduce non-linearities and are influenced by input and parametric variations [1,2]. Extensive research has been completed in the quest for improved and more robust controllers for such converters. Such systems are usually controlled using the conventional proportional integral (PI)/proportional integral derivative (PID) control, H∞ , sliding mode, predictive control, non-linear methods such as fuzzy and intelligent control, etc. Such systems are usually controlled using the conventional proportional integral (PI)/proportional integral derivative (PID) control, H∞ , sliding mode, predictive control, non-linear methods such as fuzzy and intelligent control, etc. [3,4,5]

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