Improved Transient Performance of a Fuzzy Modified Model Reference Adaptive Controller for an Interacting Coupled Tank System Using Real-Coded Genetic Algorithm

Asan Mohideen Khansadurai,Valarmathi Krishnasamy,Radhakrishnan Thota Karunakaran

doi:10.1155/2014/351973

Abstract

The main objective of the paper is to design a model reference adaptive controller (MRAC) with improved transient performance. A modification to the standard direct MRAC called fuzzy modified MRAC (FMRAC) is used in the paper. The FMRAC uses a proportional control based Mamdani-type fuzzy logic controller (MFLC) to improve the transient performance of a direct MRAC. The paper proposes the application of real-coded genetic algorithm (RGA) to tune the membership function parameters of the proposed FMRAC offline so that the transient performance of the FMRAC is improved further. In this study, a GA based modified MRAC (GAMMRAC), an FMRAC, and a GA based FMRAC (GAFMRAC) are designed for a coupled tank setup in a hybrid tank process and their transient performances are compared. The results show that the proposed GAFMRAC gives a better transient performance than the GAMMRAC or the FMRAC. It is concluded that the proposed controller can be used to obtain very good transient performance for the control of nonlinear processes.

Highlights

Industrial applications of liquid level control are found in food processing, beverage, dairy, filtration, effluent treatment, nuclear power generation plants, pharmaceutical industries, water purification systems, industrial chemical processing, boilers, and automatic liquid dispensing and replenishment devices [1]
A GA based modified MRAC (GAMMRAC) [4], an fuzzy modified MRAC (FMRAC), and a GA based FMRAC (GAFMRAC) are designed for the coupled tank setup used by [4] and their performances are compared
An adaptive controller like model reference adaptive controller (MRAC) is preferred compared to a PID controller in the study

Summary

Introduction

Industrial applications of liquid level control are found in food processing, beverage, dairy, filtration, effluent treatment, nuclear power generation plants, pharmaceutical industries, water purification systems, industrial chemical processing, boilers, and automatic liquid dispensing and replenishment devices [1]. Ziegler-Nichols and Cohen-Coon methods are widely used to tune a PID controller [2]. In these methods, the PID controller parameters are tuned for a particular operating point around which the process can be considered linear [3]. The controller would give suboptimal performance whenever the operating point is shifted out of the linearized region or when the plant parameters change due to environment and ageing. In order to have a good performance despite the environment-induced changes in the process parameters, the controller must adapt to changes in the plant dynamics. As the level process is nonlinear, [4, 5] adaptive controllers are more suitable than fixed-parameter controllers

Objectives

Results

Conclusion