Abstract: This paper presents a case study on a practical implementation of a fuzzy-PLC system for a servomechanism。 The method presented achieves smooth fuzzy control, that can be easily implemented in a PLC system。 The speed of the servomechanism is controlled。 84217

Keywords: fuzzy control, programmable logic controller (PLC), fuzzy-PLC system, process control, computer-controlled system。 

1。 INTRODUCTION 

In recent years, we have assisted at rapid changes on industries and information technologies。 Nowadays, the control of all the equipment is being performed through the use of computers。 Most equipments use Programmable Logic Controllers (PLCs) to connect with computers and to monitor each load and electricity consuming device。 PLCs are widely used in industrial control because they easy to install and very flexible in applications。 A PLC interacts with the external world through its inputs and outputs。 

In industrial automation applications, ladder logic, a programming language running on the programmable logic controllers is usually used for discrete event control。 For continuous control, PID-type controllers are more often employed (Li and Tso)。 In 1974, the first fuzzy control application appeared (Mamdani, 1974)。 Since then, fuzzylogic control (PLC) has been taken as the preferred method of designing controllers for dynamic systems, even where traditional methods can be used (Mamdani, 1993)。 

The paper presents the speed control of a servomechanism。 The fuzzy logic control (PLC) implemented on a PLC was used in order to obtain a good performance of the system。 

2。 BACKGROUND OF THE FUZZY CONTROLLER 

There are many different processes in practice and their properties are the ones to be controlled。 For instance, we want the rotation speed of a motor to be equal to a certain value for any load torque, we want planes not to fall down, we want to increase the electric power of power plants with decreasing the air pollution, we want to increase the capacity of hard discs so we have to control the reading machinery more accurately etc。 

The regulation is based on feedback control, see Figure 1。 The output of a system y(t) is measured by a sensor。 The controller computes the input of the system u(t) based on the measured output y(t) and its reference yr(t) and applies this value by an actuator to the system。

Fig。 1: The feedback control loop

Signals: y=system output    (controlled) 

yr=reference 

ε=measured error 

u=system input   (command) 

m=execution 

signal 

z=quality signal 

v=disturbance 

For a control design, the system behaviour has to be known。 For that, usually we have to describe the system by mathematical tools。 Very often, we do a physical analysis and get a system of differential and algebraic equations at first。 The second step is to determine the system inputs and outputs, to describe a so called model from the mathematical equations of the system and, if needed, to linearize it。 Then, we can measure the system parameters and write down the complete model with numerical values (Jirka Roubal)。 

When the model of the system is determined, a controller can be designed。 There are many ways to design the controller。 It depends on the performances that should be achieved (e。g。  stability of the system and quality of the system behaviour, optimal behaviour of the system according to a criterion, etc。) 

Another method to develop a controller is using the fuzzy sets also known as fuzzy aggregates instead of the numbers for the arithmetic for the fuzzy theory。 These are the mathematical based objects for which corresponding operators are defined。 

To control a process, the required data are provided by a measuring system。 Those data include the unit of measuring, the measured variable and possibly some other values which are not of interest in this case。 The unit of measuring is the physical unit i。e。 meter, whereas the measured value is a nondimensional measured result。 In order to regulate the inordinate group of all possible data they could be mapped to the group of real numbers, the corresponding number of the measured values can be used, for example。 Those numbers are representable graphically by a straight line of numbers, as shown in figure 2 (Amira)。