International Journal of Electrical and Computer Engineering (IJECE) Vol. 10, No. 3, June 2020, pp. 2463~2473 ISSN: 2088-8708, DOI: 10.11591/ijece.v10i3.pp2463-2473  2463 Journal homepage: http://ijece.iaescore.com/index.php/IJECE An educational fuzzy temperature control system Peshraw Salam 1 , Dogan Ibrahim 2 1 Department of Computer Science, University of Garmian kalar, Sulaymaniyah Kurdistan, Iraq 2 Department of Computer Information Systems, Near East University, Turkey Article Info ABSTRACT Article history: Received Apr 3, 2019 Revised Nov 24, 2019 Accepted Des 9, 2019 Control engineering is one of the important engineering topics taught at many engineering based universities around the world in most undergraduate and postgraduate courses. The control engineering curriculum includes both the classical feedback based control theory and the state space theory. The modern control theory is based on the intelligent control algorithms utilizing the soft computing techniques, such as the fuzzy control theory and neural networks. Laboratory work is an important part of any control engineering course. The problem with the modern control theory laboratories is that it is essential to offer simple experiments to students so that they can easily put the complex theories they have learned in their courses into practice and see and understand the results. This paper describes the design of a low-cost fuzzy based microcontroller temperature control system using off the shelf products. The developed system should provide a low-cost fuzzy control experiment in the laboratories for students studying control engineering. Keywords: Educational control system Fuzzy control Microprocessor based control temperature sensor Temperature control Copyright © 2020 Institute of Advanced Engineering and Science. All rights reserved. Corresponding Author: Dogan Ibrahim, Department of Computer Information Systems, Near East University, Lefkosa, Mersin 10, Turkey. Email: dogan.ibrahim@neu.edu.tr 1. INTRODUCTION Temperature is one of the most important parameters in industrial process control, particularly in chemical engineering plants. Temperature control systems in general have nonlinear, time varying, and long time delay characteristics. Accurate control of the temperature has been an important issue in many years in the field of industrial temperature control. The automatic control theory is a complex mathematical field and it continues to grow both in terms of theory and applications [1]. Control theory has evolved in three basic stages [2]: First, on/off type simple control dominated the field where the controller output is either fully on if you are below the set-point, or fully off if you are at the set-point or above. This type of control was very easy and required no system modelling or complex mathematics, but it had many serious disadvantages such as oscillations around the set-point which gave rise undesirable behaviour [3]. Also on/off type control can damage actuators and pumps as they are rapidly switched on and off. Second, the PID based control [4] has dominated the control field for many years and the majority of industrial processes have currently been implemented with some form of PID control. Here, the controller output is derived from a signal which is proportional to the error signal, its integral, and its derivative. The PID algorithm has the advantage in implementing and providing precision controls, even in the presence of external disturbances [3]. Third, the artificial intelligence (AI) and decision-based control has become popular in the last decade as an alternative to conventional control. AI-based control systems have demonstrated to have learning and decision- making capabilities that are not possible in using conventional control theory [5]. Some of the commonly used artificial intelligence methods are: neural networks, fuzzy logic, genetic algorithms, probabilistic methods, and evolutionary computing. Artificial neural networks are being used to solve complex modelling, identification,