Comparative Performance Evaluation of Fractional Order PID Controller for Heat Flow System Using Evolutionary Algorithms

Comparative Performance Evaluation of Fractional Order PID Controller for Heat Flow System Using Evolutionary Algorithms

Rosy Pradhan (Veer Surendra Sai University of Technology, Burla, Odisha, Sambalpur, India) and Bibhuti Bhusan Pati (Veer Surendra Sai University of Technology, Burla, Sambalpur, India)
Copyright: © 2019 |Pages: 23
DOI: 10.4018/IJAMC.2019100105
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The main objective of the paper is to design of Fractional order PID (FOPID) controller for heat flow system using evolutionary algorithms. The recent developed metaheuristic algorithms such as Ant Lion Optimizer (ALO), Grey Wolf Optimizer (GWO) and Moth Flame Optimizer (MFO) are used in the FOPID design. The FOPID design for heat flow system is formulated as an optimization problem to minimize different indices error such as Integral Absolute Error (IAE), Integral Squared Error (ISE), Integral Time Absolute Error (ITAE), Integral Time Squared Error (ITSE). The evolutionary algorithm based FOPID control performances are compared with PID control performance for the heat flow system. In addition, the proposed methods have superiority value in terms of transient and frequency domain analysis than the traditional and PID methods. Comparative performance evaluation of meta-heuristic based FOPID design for heat flow system has not carried out before.
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1 Introduction

Recent advancement in control system engineering have gain the focus of researchers in designing of proportional integral derivative (PID) and fractional proportional integral derivative (FOPID) controllers from past few decades (Lin et al., 2016; Pradhan et al., 2016; Pradhan et al., 2017; Pradhan et al., 2015; Sain et al., 2016). Despite of advances in control technique, the PID controller is still widely used in the industry due to its demonstrated advantages such as easy to understand as it has only three tunable parameters, simple structure and ease in implementation (Astrom and Hagglund, 2001; Ogata, 2002; Visioli, 2012; Tan et al., 2006; Hagglund and Astrom, 2002). In recent days, modern control theories have made much advancement in the fields of designing of PID controller by using the idea of fractional calculus to improve its performance in various industrial systems. The fractional-order-proportional- integral- derivative (FO-PID) controller is a generalized representation of the classical PID controller. FO-PID provides a better response than the integer order PID both for integer order system and fractional order systems in many industries (Samko et al., 1993). Also, it exhibits a better robustness performance against integer order PID controller (Oustaloup, 1991). However, tuning of FO-PID poses a challenging problem due to its additional tunable parameters, i.e., five parameters to select instead of three parameters in a standard classical PID controller. Therefore, many advanced control tuning methods have been developed in recent years to solve the difficulties arises in FO-PID controller design using fractional calculus (Oustaloup, 1991; Samko et al., 1993; Aghababa, 2016; Hamamci, 2007). As evolutionary optimization algorithms based design does not depends on the rigorous mathematical model of the plants, recently, many research works have been carried out by using different evolutionary optimization algorithms (Aghababa, 2016; Hamamci, 2007; Ramezanian et al., 2013; Biswas et al., 2009; Lee et al., 2010; Verma et al., 2017; Ates and Yeroglu, 2016; Cao et al., 2005; Zamani et al., 2017; Aghababa, 2016; Oprzedkiewicz and Dziedzic, 2017; Raju et al., 2016; Haji and Monje, 2017; Altintas and Aydin, 2017; Shata et al., 2016; Chaib et al., 2017; Bouarroudj, 2015). In this regard, to design FO-PID controller, either Genetic algorithm (Zhang and Li, 2011), Fuzzy logic (Moafi et al., 2016), Particle swarm optimization (PSO) (Zamani et al., 2009) or hybrid optimization (Lapa, 2017) have been used. Among them PSO based design is widely used due to its faster convergence, however, it often exihibits local solution. Whereas Genetic algorithm achieves global solution at the cost of high computational burden. Therefore, further attempts have been made to propose an alternate algorithm to achieve faster convergence as well as global solution. In this paper, three meta-heuristic algorithm such as Grey Wolf Optimization (GWO), Antlion Optimizer (ALO) and Moth Flame Optimization (MFO) has been used due to proven advantages discussed below.

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