International Journal of Computer Applications (0975 – 8887) Volume 179 – No.30, March 2018 15 Comparative Study of Control Methods’s Application for Pneumatic System in Simulation Environment E. Ntimeri Piraeus University of Applied Sciences, Dept. of Automation Engineering Christos Drosos, PhD Piraeus University of Applied Science, Dept. of Automation Engineering D. Tseles Professor Deputy President P. Ralli & Thivon 250 12244 Aegaleo, Athens ABSTRACT In light of the rapid rates of technology development in our times, there has been a continuous effort to introduce everyday technological advances in order to cover better and easier human needs. Especially in the sector of research and applications, the need for simulation programs was seen as offering security to errors, reduce costs and they are accessible to use by professionals and higher education students. This thesis will present the study of control methods’s application for pneumatic system in simulation environment. In addition, it will analyze and describe the operation of the pneumatic system and all the testing methods used in it. The object of the study, which comes with MSc, since it deals with modern automation technology applications, will try to cover questions such as whether these control methods are appropriate and effective for the pneumatic system which is studied, and especially how effective it is the use of a Lookup table to Implement Fuzzy Controller (Fuzzy) with Proportional-Integral-Derivative Controller (PID). For the use of this Lookup table in the pneumatic system of the present study, results have not been extracted to date. These two events are the main purpose of this thesis, in an attempt to perform control of the system on the applications above. The main part of the study will explain the design of the system as well as the type of controllers and the Lookup table. The way of connecting and operating among the pneumatic system, the control methods and the Lookup table, in the simulation environment will be presented in detail, while all this will emerge the conclusions of use specific control methods, the advantages and disadvantages will be discussed and both will be proposed optimizations to further expanding benefits of their operation. As a possible result of using these methods will occur to achieve the optimal and efficient control of the system. General Terms Simulation Keywords Pneumatic System, Control Methods, Simulink, PID Controller, Lookup Table FuzzyPID Controller 1. INTRODUCTION Based on the rapid growth of technology, a perpetual effort has been made to bring technological breakthroughs into everyday life so that human needs are met in the best possible way. More specifically, in the field of industry, research and applications, the need for simulation programs was observed, because they are safe for potential errors, minimize costs and are fairly affordable in terms of both professionals and higher education students. This paper will present the study of the application of pneumatic control methods in a simulation environment. In addition, the function of the mental system being studied, as well as all the control methods used in it, will be analyzed and described. The subject of the study will attempt to answer questions such as whether the specific control methods are appropriate and effective for the particular mental system, and most importantly how efficient is the use of a Fuzzy Matching Proportional -Integral- Derivative Controller (PID). 1.1 Controllers Proportional Controller (P) A Proportional (P) Controller (Proportional Control) gives the signal output corresponding to the error received at the input. In essence, it is a gain amplification device with KR gain. A Proportional Controller increases the response rate of the system, but it is likely to create a permanent state error depending on the system type, and for zero error it will also have zero output. [15] Integral Controller (I) Integral Controller I (Integral Control) gives the output a signal Proportional out to the integrity of the error it receives at the input. Also contains Ki parameter, which has a unit of measurement of 1 sec and is called an integration factor. Each system using the integral controller I, from one system has been converted to another system where the value of the integration coefficient is similar to a physical frequency of the system. As the integration coefficient increases, the physical frequency of the system increases resulting in a faster response of the system and a decrease in the depreciation factor. [15] Derivative Controller (D) Derivative Control D (Derivative Control) when at the input of the error signal is in the form of step excitation, the output of the controller is the impulse function with a theoretically infinite width for t = 0. If the error is stable then the output of the Derivative controller is zero. The Derivative Controller limits the error to the permanent state at the stage of the transient response of the systems, but in practice it is never used by itself. [15] Proportional - Integral Controller (PI) The Proportional-Integral (PI Controller)sums up the proportional and total control. The use of the integration term is intended to eliminate the error in the permanent state and change the DC gain of the system. The Proportional use term improves the stability of the system and increases the response rate. [15] Proportional - Derivative Controller (PD) The Proportional - Derivative (PD) Controller combines the Proportional use and the Derivative Controller cumulatively. A Proportional-Derivative Controller (PD) controller increases system damping and limits its elevation, but does not directly reduce the permanent error. Using the derivative