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Monday, July 30, 2007

Application of Fuzzy Logic in Fully Automatic Washing Machine

Fuzzy control, which directly uses fuzzy rules is the most important application in fuzzy theory. Using a procedure originated by Ebrahim Mamdani in the late 70s, three steps are taken to create a fuzzy controlled machine:

1.Fuzzification(Using membership functions to graphically describe a situation)
2.Rule evaluation(Application of fuzzy rules)
3.Defuzzification(Obtaining the crisp results)

To build a more fully automatic washing machine with self determining wash times, we are going to focus on two subsystems of the machine: (1) the sensor mechanism and (2) the controller unit. The sensor system provides external input signals into the machine from which decisions can be made. It is the controller's responsibility to make the decisions and to signal the outside world by some form of output. Because the input/output relationship is not clear, the design of a washing machine controller has not in the past lent itself to traditional methods of control design. We address this design problem using fuzzy logic.

Input/Output of Controller

Fuzzy logic controller have two inputs: (1) one for the degree of dirt on the clothes and (2) one for the type of dirt on the clothes. These two inputs can be obtained from a single optical sensor. The degree of dirt is determined by the transparency of the wash water. The dirtier the clothes, the lower the transparency for a fixed amount of water. On the other hand, the type of dirt is determined from the saturation time, the time it takes to reach saturation. Saturation is the point at which the change in water transparency is close to zero (below a given number). Greasy clothes, for example, take longer for water transparency to reach saturation because grease is less water soluble than other forms of dirt. Thus a fairly straightforward sensor system can provide the necessary inputs for our fuzzy controller.

Definition of Input/Output Variables

Before designing the controller, we must determine the range of possible values for the input and output variables. These are the membership functions used to translate real world values to fuzzy values and back. Figure below shows the labels of input and output variables and their associated membership functions. Note that wash time membership functions are singletons (crisp numbers) in this example. We can use fuzzy sets or singletons for output variables. Singletons are simpler than fuzzy sets. They need less memory space and work faster. If we could not be satisfied by the result when output values are given by singletons we could change them into fuzzy sets. We should use Mandani's method for inference if we want to define output values as fuzzy sets.

Linguistic variables & their ranges

Fuzzy logic for Dirtiness &Type of dirt input

Linguistic variable: Dirtiness, D
Linguistic value Notation Numerical range
Small S [0.00, 50.00]
Medium M [0.00, 100.00]
Large L [50.00, 150.00]

Linguistic variable: Type of dirt, TD
Linguistic value Notation Numerical range
Not greasy NG [0.00, 50.00]
Medium M [0.00, 100.00]
Greasy G [50.00, 150.00]


The decision making capabilities of a fuzzy controller are codified in a set of rules. In general, the rules are intuitive and easy to understand, since they are qualitative statements written in English like if-then sentences. Rules for our washing machine controller are derived from common sense, data taken from typical home use, and experimentation in a controlled environment. A typical intuitive rule is as follows:

The rule table




Wash time





















































































Saturday, July 21, 2007

How Fuzzy Logic is Applied

Fuzzy logic usually uses IF/THEN rules, or constructs that are equivalent, such as fuzzy associative matrices.

Rules are usually expressed in the form:
IF variable IS set THEN action

For example, an extremely simple temperature regulator that uses a fan might look like this:

IF temperature IS very cold THEN stop fan
IF temperature IS cold THEN turn down fan
IF temperature IS normal THEN maintain level
IF temperature IS hot THEN speed up fan

Notice there is no "ELSE".

All of the rules are evaluated, because the temperature might be "cold" and "normal" at the same time to differing degrees.

The AND, OR, and NOT operators of Boolean logic exist in fuzzy logic, usually defined as the minimum, maximum, and complement; when they are defined this way, they are called the Zadeh operators, because they were first defined as such in Zadeh's original papers. So for the fuzzy variables x and y:

NOT x = (1 - truth(x))
x AND y = minimum(truth(x), truth(y))
x OR y = maximum(truth(x), truth(y))

There are also other operators, more linguistic in nature, called hedges that can be applied. These are generally adverbs such as "very", or "somewhat", which modify the meaning of a set using a mathematical formula.

Human beings make decisions based on rules. Even though, we may not be aware of it, all the decisions we make are based on computer like if-then statements. If the weather is fine, then we may decide to go out. If the forecast says the weather will be bad today, but fine tomorrow, then we make a decision not to go today, and postpone it till tomorrow. Rules associate ideas and relate one event to another.

Fuzzy machines which always tend to mimics the behavior of man work the same way. Only this time the decision and the means of choosing that decision are replaced by fuzzy sets and the rules are replaced by fuzzy rules. Fuzzy rules also operate using a series of if-then statements. For instance, X then A, if y then b, where A and B are all sets of X and Y. Fuzzy rules define fuzzy patches, which is the key idea in fuzzy logic.

A machine is made smarter using a concept designed by Bart Kosko called the Fuzzy Approximation Theorem (FAT). The FAT theorem generally states a finite number of patches can cover a curve as seen in the figure below. If the patches are large, then the rules are sloppy. If the patches are small then the rules are fine.

Thursday, July 19, 2007

The Fuzzy Logic

Fuzzy logic is an extension of Boolean logic dealing with the concept of partial truth. Whereas classical logic holds that everything can be expressed in binary terms (0 or 1, black or white, yes or no), fuzzy logic replaces Boolean truth values with degrees of truth.

Degrees of truth are often confused with probabilities, although they are conceptually distinct, because fuzzy truth represents membership in vaguely defined sets, not likelihood of some event or condition. Fuzzy logic allows for set membership values between and including 0 and 1, shades of gray as well as black and white, and in its linguistic form, imprecise concepts like "slightly", "quite" and "very". Specifically, it allows partial membership in a set. It is related to fuzzy sets and possibility theory. It was introduced in 1965 by Prof. Lotfi Zadeh at the University of California, Berkeley.

Fuzzy logic is controversial despite wide acceptance: it is rejected by some control engineers for validation and other reasons, and by some statisticians who hold that probability is the only rigorous mathematical description of uncertainty. Critics also argue that it cannot be a superset of ordinary set theory since membership functions are defined in terms of conventional sets.

Many people would note that fuzzy logic sounds good, but how is it being used. A good example is a fuzzy washing machine. Using yes and no logic to make a washing machine that would automatically handle all the controls on a load of wash, would add hundreds of dollars to the cost of the machine. Dozens of special sensors and the equivalent of a small computer would have to be added to the machine. The fuzzy washing machines being sold in Japan for the last few years cost about twenty dollars more, use a handful of inexpensive sensors and a small logic chip. All you have to do to use the machine, in many cases, is put the clothes in and turn it on.
Most scientists refused to look closely at the logic, when it was first talked about. The ideas seemed too radical to them. It took engineers first in Europe, but mainly in Japan to start using fuzzy logic before scientists started to take it seriously.

Other applications of Fuzzy logic such as:

  • Automobile subsystems, such as ABS and cruise control
  • Air conditioners
  • Cameras
  • Digital image processing, such as edge detection
  • Rice cookers
  • Dishwashers
  • Washing machines and other home appliances.

Saturday, July 14, 2007

Role of Sensor in CIM

This post will tell about the role of sensor in computer integrated in manufacturing. Since we know that the computer integrated manufacturing is based on the manufacturing system that has been implemented by the computer based knowledge. Since the sensor is the most linkable to the automation, the sensor is applying their role in manufacturing system through the computer integrated manufacturing. Based on the role of sensor, we can see that the sensor is actually give the most function to the system to make it work as an automation in computer integrated manufacturing.

Process measurands associated with sensor signal types

Signal Output Type

Associated Process Measurands

Mechanical (includes acoustic)

Position (linear, angular)




Stress. pressure


Mass. density

Moment, torque

Flow velocity, rate of transport

Shape, roughness, orientation

Stiffness, compliance


Crystallinity, structural integrity

Wave amplitude. phase, polarization, spectrum

Wave velocity


Charge, current

Potential. potential difference

Electric field (amplitude, phase. polarization. spectrum)




Magnetic field (amplitude. phase. polarization, spectrum) Magnetic flux


Chemical (includes biological)

Components (identities, concentrations, states) Biomass (identities, concentrations, states)








Wave amplitude, phase, polarization, spectrum

Wave velocity




Specific heat

Thermal conductivity

Role of Sensor in Computer Integrated Manufacturing

The role of sensor systems for computer integrated manufacturing is generally composed of sensing, transformation / conversion signal processing, and decision making, as shown in figure below. The output of the sensor system is either given to the op­erator via a human-machine interface or directly utilized to control the machine. Objectives, requirements, demands, boundary conditions, signal processing, com­munication techniques, and the human-machine interface of the sensor system are described in this section.

Role of sensor in computer integrated manufacturing.

Manufacturing system part

Role of sensors

Machine tools and robot

Position measurement

Sensor of orientation

Calibration of machine tools and robot

Collision detection

Machine tools monitoring and diagnosis


Optical measuring

Light-section method

Tactile measuring

Process monitoring

Temperature controlling

Dosage and level controlling

Process quantity

Process quality

Tuesday, July 10, 2007

MTAB Aristo Robot

This MTAB Aristo robot is the first robot that i have been operated during my calss session. From the robot operation that I obtain, I have finished the tasks and able to program the Aristo 6-axes robot according to their required tasks or problem. From the task I must select the low speed of the robot arm because of the high speed will cause a momentum to the robot arm that makes the arm cannot go to the point or position exactly like what we have been write in the program. The high speed also dangerous for the robot because if the robot arm is hit object or human with high speed it will cause severe damage.

In every program, I have put the command HOME ALL and the end of the program is to make sure that the robot is already home the initial position and this will make easy to the robot in making the next movement or execute the other programs. HOME ALL command is use to make the robot arm to come back to the initial position and reset the position of origin in every joint.

The program of the robot is like as C or C++ program that include the command of looping process by using conditional commands like IF, JUMP and LABEL. This all commands is use to loop or repeat the program again. If 'S' is equal to "zero" the program is execute once than the command ADD S = S + 1 is to add the S = "zero" with one and overwrite the answer in 'S' so that for the conditional command IF, when the answer of 'S' is lower that the value of the conditional set the program is execute again and jump to the LABEL declared. While the S will add again and when the conditional command is not fulfill, so that the program will pass the conditional command and execute the next command.

Below is the procedure or safety guide to be taken during operate a robot:

a) All connections to PC, power control unit and robot is in place.
b) The power and air pressure is on.
c) Safety:
i. Be careful not to collide or being touched by the robot to avoid injury
ii. Be aware of robot movement, close monitoring is given to the robot when it makes a move to prevent
from damaging the structure of the robot.
d) Caution:
i. All programs is test at low speed.
ii. Emergency press of is pressed when necessary.
iii. All axes are homed (HOME ALL) every time before running in the new programs.
iv. The robot is don’t to be touched during their operation or movement.

Thursday, July 5, 2007

Intelligent Robot

What is an intelligent robot? Intelligent robot is the machine that can think their operation by itself. Since the machine have a ‘brain’ to think like a machine (robot) with neuro-fuzzy system or genetic algorithm to implement the input to get their output according to the programmed system of the machine (robot).

The vedeo below is one of the robot arm that been use during my CIM laboratory session. the robot procedure as stated below

The vedeo show the robot movement during bolting process. the operation done for 4 bolt operation but the operation shown is not fully bolt feeding process, just the movement of the end effector of the robot arm to each point.

Wednesday, July 4, 2007

System Stability Using MATLAB Software 2

In this chapter we explored the concepts of system stability from both the classical and the state-space viewpoints. We found that for linear systems, stability is based on a natural response that decays to zero as time approaches infinity. On the other hand, if the natural response increases without bound, the forced response is overpowered by the natural response, and we lose control. This condition is known as instability. A third possibility exists: the natural response may neither decay nor grow without bound but oscillate. In this case the system is said to be marginally stable.

We also used an alternative definition of stability when the natural response is not explicitly available. This definition is based on the total response and says that a system is stable if every bounded input yields a bounded output (BIBO) and unstable if any bounded input yields an unbounded output.

Mathematically, stability for linear, time-invariant systems can be determined from the location of the closed-loop poles:

• If the poles are only in the left half-plane, the system is stable.
• If the poles are only in the right half-plane, the system is unstable.
• If the poles are on the jω-axis and in the left half-plane, the system is marginally stable as long as the
poles on the jω-axis are of unit multiplicity; it is unstable if there are any multiple jω-axis.

In this post we will look for the stability in aeroplane by looking to the control system block diagram below

The block diagram represent the system for determining the angle (theta) of the aerofoil in aeroplane system in order to produce the output from the desire input.

After getting the equation from the diagram, with matlab we can find the stability of the system by determining the value K (Gain) from Routh-Hurwitz Criterion

From the program and result above, we can use the MATLAB Simulation program to look either the gain that we get from the calculation can be use through the system. the value K (gain) is inserted through the simulated block diagram and the graph will show the final value which consist the transient response and the stready state error of the system

Monday, July 2, 2007

System Stability Using MATLAB Software 1

Stability is the most important system specification. If a system is unstable, transient response and steady-state errors are moot points. An unstable system cannot be designed for a specific transient response or steady-state error requirement. There are many definitions for stability, depending upon the kind of system or the point of view.

  • A linear, time-invariant system is stable if the natural response approaches zero as time approaches infinity.
  • A linear, time-invariant system is unstable if the natural response grows without bound as time approaches infinity.
  • A linear, time-invariant system is marginally stable if the natural response neither decays nor grows but remains constant or oscillates as time approaches infinity.

Thus, the definition of stability implies that only the forced response remains as the natural response approaches zero. a stable system needed because in order to build certain system such as an aerofoil in aeroplane system a stable system play most important part in building in because the unstable system will make the whole part of the object that we will build is dangerous to human.