International Journal of Computer Applications (0975 – 8887) Volume 104 – No.6, October 2014 32 A Comparison between a Fuzzy and PID Controller for Universal Motor Abdelfettah Zeghoudi URMER Research unit, Tlemcen University, B.P. 119, Tlemcen, Algeria Ali Chermitti URMER Research unit, Tlemcen University, B.P. 119, Tlemcen, Algeria ABSTRACT This paper describes the use of PID controller, and fuzzy logic controller techniques to control of a motor. Using Matlab/Simulink, This work seeks to identify the strengths and weaknesses of each of the two pilots, for the fuzzy logic controller (Intelligent Control). The system performance is evaluated in comparison with a traditional PID control scheme. Both simulation and experimental results are presented. Keywords PID Control; Fuzzy Logic Controller; Control of a Motor. 1. INTRODUCTION It is well known that the control of many industrial processes characterized by highly nonlinear dynamics and parameter uncertainties does not give satisfactory responses using conventional controllers. It is true that the fuzzy logic controllers have been reported to be successfully used for a number of complex and nonlinear processes such as motor control. Souad Rafa et al [1] proposed a new fuzzy vector control of induction motor; Jaime Fonseca et al [2] used a fuzzy logic technique to control of an induction motor; Gerasimos G. Rigatos [3] designed an Adaptive fuzzy control of DC motors…. The PID controller is a self regulating system (closed loop), which seeks to reduce the error between the set point and the process [4]. PID controllers are commonly used for industrial process control, there are used many applications, for example, by controlling the temperature of a boiler for controlling an AC motor. It has always been the question of the applicability of this pilot process are nonlinear in nature as dynamic works in which a PID controller is to control industrial processes and this is hard involved the use of intelligent control fuzzy logic. Fuzzy logic approach allows the designer to handle efficiently very complex closed-loop control problems, reducing, in many cases, engineering time and costs [4, 5]. Fuzzy control also supports nonlinear design techniques that are now being exploited in motor control applications [6, 7, 8]. This paper is organized as follows: Section 2 introduces of the fuzzy logic characteristics; Section 3 describes the design of a PID controller, in Section 4 simulation results of two process fuzzy logic and PID controller are presented, section 6 is for analysis of results, the conclusions presented in Section 7. 2. FUZZY CONTROLLER There are specific components characteristic of a fuzzy logic controller (FLC) to support a design procedure. Figure 1 shows the controller between the preprocessing block and post processing block. [9] Fig 1: Structure of fuzzy logic controller 2.1 Preprocessing The inputs are most often hard or crisp measurement from some measuring equipment rather than linguistic. A preprocessor, the first block in Figure 1 shows the conditions the measurements before enter the controller [9]. 2.2 Fuzzification The first block inside the controller is fuzzification which converts each piece of input data to degrees of membership by a lookup in one or several membership functions. The fuzzification block matches the input data with the conditions of the rules to determine. There is degree of membership for each linguistic term that applies to the input variable [9]. 2.3 Rule Base The collection of rules is called a rule base. The rules are in “If Then” format and formally the If side is called the conditions and the Then side is called the conclusion. The computer is able to execute the rules and compute a control signal depending on the measured inputs error (e) and change in error (dE). In a rule based controller the control strategy is stored in a more or less natural language. A rule base controller is easy to understand and easy to maintain for a non- specialist end user and an equivalent controller could be implemented using conventional techniques [9]. 2.4 Defuzzification Defuzzification is when all the actions that have been activated are combined and converted into a single non-fuzzy output signal which is the control signal of the system. The output levels are depending on the rules that the systems have and the positions depending on the non-linearities existing to the systems. To achieve the result, develop the control curve of the system representing the I/O relation of the systems and based on the information; define the output degree of the membership function with the aim to minimize the effect of the non- linearity [9].