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Speed Of DC Motor Controlling Using PLC

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Dogus Cimen

on 28 December 2013

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Transcript of Speed Of DC Motor Controlling Using PLC

Speed Of DC Motor Controlling Using PLC

CONTENT

INTRODUCTION
GENERAL INFORMATION ABOUT CONTROL SYSTEM AND CONTOL SYSTEM TOOLBOX
TRANSFER FUNCTION
PARTS OF THE CONTROL SYSTEM TOOLBOX
TRANSFER FUNCTIONS’ WRITTEN ON MATLAB
BLOCK DIAGRAM OF UNCONTROLLED SYSTEM
SPEED OF DC MOTOR CONTROL FOR THIS PROJECT ON MATLAB
CONCLUSION

INTRODUCTION
This project is to design and to control a dc motor of speed control systems using algorithm of Proportional Integral Derivative (PID).

This dc motor is controlled by a modern computerized control system, which is programmable logic controller (PLC).

A Programmable Logic Controller (PLC) or Programmable Controller is a digital computer used for automation of industrial processes, such as control of machinery on factory assembly lines. PLCs are used in many different industries and machines such as packaging and semiconductor machines. The program is stored in battery-backed memory and/or EEPROMs (Electronically Erasable Programmable Read-Only Memory). It can often control complex sequencing and is often written by engineers.

Unlike general-purpose computers, the PLC is designed for multiple inputs and output arrangements, extended temperature ranges, immunity to electrical noise, and resistance to vibration and impact.


CONTROL SYSTEMS

WHY WE USE CONTROL SYSTEM TOOLBOX?



The linear systems can be defined in various formats such as transfer-function, state-space, pole-zero-gain, and frequency-response models. This section will describe elementary tools commonly used in control system analysis and design.

TRANSFER FUNCTION

s=Laplace variable
N=Numarator of Polynomial
D=Denominator of Polynomial
Submitted by :
GÖRKEM BAKIR
MEHMET KIZILASLAN
DOĞUS ÇİMEN


Submitted to :
Prof. Dr. İBRAHİM H. GÜZELBEY
Matlab For Engineers
Speed Of DC Motor Controlling Using PLC
Control System Toolbox

The control toolbox provides algorithms and tools for solving various problems in control system design. This includes analyzing linear systems and controller design.
A control system is a device, or set of devices to manage, command or regulate the behavior of the system.
There are two types of system. One of them is open-loop system and the other one is closed-loop system. In open-loop system there is no way of the comparing actual output and desired output. But closed-loop system has feedback so you can compare the actual and desired output.
Control system has very large application range. Some of the examples are speed control of an automobile, traffic control system, pos system, heat control system etc…
To create a transfer function model;

G=tf(num,den)

where num and den are row vectors of coefficients of the polynomials N(s) and D(s), respectively. These row vectors are ordered in descending powers of s. G is a tf model object.

HOW CAN WE DETERMINE TRANSFER FUNCTION

When we enter the tf, num and den commands into the command window we will get the transfer function. For example;

num=1;
den=[1 4 9];
h=tf(num,den
Roots of the numarator is called as zeros and roots of the denominator is callad as poles of the system.

PARTS OF CONTROL SYSTEM TOOLBOX

1-) LTI Viewer (Linear Time-Invariant)
LTI Viewer is a graphical user interface (GUI) that simplifies the analysis of linear, time-invariant systems. We can generate time and frequency response plots to inspect key response parameters, such as rise time, maximum overshoot, and stability margins.

Step plots the model's response to a step input
You can display the following information in the step response:
Peak Response — The largest deviation from the steady-state value of the step response
Settling Time — The time required for the step response to decline and stay at 5% of its final value
Rise Time — The time require for the step response to rise from 10% to 90% of its final value
Steady-State — The final value for the step response
2-) SISO Design Tool

The SISO Design Tool is a graphical-user interface (GUI) to design compensators.

The SISO Design Tool has the following components:

The SISO Design Task Node in the Control and Estimation Tools Manager, a user interface (UI) that facilitates the design of compensators for single-input, single-output feedback loops through a series of interactive panes.

Optimization-based tuning methods that automatically tune the system to satisfy design requirements

IMPORT PROCESS FOR THE SISO DESIGN FOR SISO DESIGN TASK

We can import models for the plant (G), compensator (C), prefilter (F), and/or sensor (H). G or H or both are LTI models or row or column arrays of LTI models. If both G and H are arrays, their sizes must match.
To import a model:
1-We should select a system in the System column and click Browse. The Model Import dialog box opens, as shown in the next figure.
Select a model from the Available Models list. We can import models from:
The MATLAB workspace
A MAT-file
Then we should select Import, then click Close. We can now see the model loaded into the system selected in the System Data dialog.
Click OK. The Graphical Tuning window is updated with the model we loaded
SISO design for SISO design task window has a right click property. By clicking right you can easily add; real or complex pole or zero. If there is a steady state error in the system, by adding the integrator from the right click menu error can be eliminated.
3-)Preferences

The SISO Tool Preferences provides to change the frequency units on all the Bode plots created in the SISO Design Tool from rad/s to Hertz, select SISO Tool Preferences from the Edit menu in the SISO Design Task node on the Control and Estimation Tools Manager, as you see.

Transfers Functions’ Written On Matlab

1-) Represantation of transfer function;

a) num=[0 0 25];
den=[1 4 25];
sys=tf(num,den)

b) num=30*[1 -5 3]
den=poly([-1 -2 -3 -4 -5])
sys=tf(num,den)

2-) Finding poles and zeros of a system;

a) sys=tf([10 50],[1 7 20 100]);
----------
p=pole(sys)
z=zero(sys)
pzmap(sys)

By using pzmap command we can get the poles and zeros o the screen

We can get the poles and zeros by using sisotool command;

Sisotool
File-import G and H
Ok
G=tf([0 0 10],[1 2 10]) H=tf([0 5],[1 5])

3-) Finding the step response chart
ltiview
file-import
v=tf([25],[1 4 25])

We can get the same step response by using step command;

v=tf([25],[1 4 25]);
step(v)

Block Diagram Of Uncontrolled System (DC Motor Modeling Without Controller)
The value of transfer function 1 is similar to the DC motor plant. To get the transfer function 1, convert the state space equation that we obtain from the DC motor modeling earlier to a transfer function by applying the Laplace transform to the state equation and output equation.

Then ; we can write laplace transform for controlling dc motor without controller as following calculations :
Controller Type Of This Project
In this project; The main contribution is the algorithm of PID controller. A proportional-integral-derivative controller (PID controller) is a generic control loop feedback mechanism widely used in industrial control systems.

The PID controller calculation 2 involves three separate parameters; the Proportional, the Integral and Derivative values. The Proportional value determines the reaction to the current error, the Integral value determines the reaction based on the sum of recent errors, and the Derivative value determines the reaction based on the rate at which the error has been changing. The PID controller compares a measured value from a process with a reference set point value. The difference (error) is then used to calculate a new value for a manipulatable input to the process that brings the process measured value back to its desired set point. Unlike simpler control algorithms, the PID controller can adjust process outputs based on the history and rate of change of the error signal, which gives more accurate and stable control. PID controllers do not require advanced mathematics to design and can be easily adjusted (tune) to the desired application.The purpose to design this project is to overcome the problem in industry like to avoid machine damages and to avoid slow rise time and high overshoot. This is because when the starting voltage is high, it is not suitable for machine and can make machine damages. So, we use PID controller to overcome this problem
% This program will simulate the Armature controlled DC Motor Transfer
% funcations along with the step out waveform
disp('In order to use the program all motor parameters must be converted to SI units')
disp('If the maxium operating speed is not given please enter zero')
wmax=input('What is the maximum operating speed, Wmax(rad/s)>');
Imax=input('What is the maximum armature current, Imax(A)>');
Ke=input('What is the Voltage constant, Ke(V*s/rad)>');
Kt=input('What is the Torque constant, Kt(N*m/A)>');
Tf=input('What is the Friction torque, Tf(N*m)>');

R=input('What is the Armature resistance, R(ohms)>');
L=input('What is the Armature inductnace, L(H)>');
Jm=input('What is the Armature moment of inertia, Jm(N*m*s^2/rad)>');
Bm=input('What is the Armature viscous friction, Bm(N*M*s/rad)>');
disp('Does the motor have speed reducers and load specifications?');
ans=input('Enter 1 for YES; Enter 0 for No>')

if ans==1;

N1=input('How many teeth are on the first(N1) speed reducer>');
N2=input('How many teeth are on the second(N2) speed reucer>');
JL=input('What is the Load inertia (JL)>');
BL=input('What is the Load friction (BL)>');

disp('The Mechanical time constant is (sec)')
Tm=Jm/Bm
pause

disp('The Electrical time constant is (sec)')
Te=L/R
pause
disp('The Voltage-driven Transfer Funcation of the Motor (OhmM(s)/Ea(s)')

It is go on…


Control systems have very large of application range in daily life.
With control system toolbox ; we can control everything in daily life.For instance,for eye surgery;control system can be written for machine doing eye surgery and then it is used as zero error for controlling with control system toolbox.
Finally, By using matlab we can easily build the model of the block diagrams of the system and we can easily calculate transfer function of the system. Transfer function is the key part of the this toolbox. By using control toolbox we can calculate frequency, roots, damping ratio and error of the system and then we can control everything with this method.

We are appreciate for your interest and time
Conclusion
Step Response
Full transcript