IBSI/XII/2010 - 24

Ibnu Sina: 3rd Assignment

Ibnu Sina: 3rd Assignment
Membuat makalah tentang Logika Fuzzy

Dikumpulkan selambat-lambatnya pada January 6, 2011; dalam bentuk soft-copy yang dikirim ke email: ariel.ismail.LET@gmail.com

KETERANGAN

Jumlah kata adalah 1000 - 1500, dan ditulis dalam bahasa Indonesia ataupun English Language, dengan TIDAK LUPA menyebutkan sumber/reference- nya.

Isi makalah mengandungi 5 sub-bab (dibuat secara berurut):

1. Pengenalan Logika Fuzzy

2. Dasar-dasar Logika Fuzzy

    2.1. Himpunan dan keanggotaan fuzzy (Membership Function)

    2.2. Operasi dasar fuzzy

    2.3. Aturan IF-THEN

3. Bagaimana memfungsikan Logika Fuzzy pada Sistem Pakar

4. Proses penalaran fuzzy: Fuzzifikasi, Inferensi, dan Defuzzifikasi

5. Arsitektur Sistem Fuzzy dan cara kerja

Pada bahagian ke-4, HARUS memuat gambar dan penjelasan bagaimana proses penalaran pada Fuzzy System.

HARUS menyebutkan sumber penulisan (reference) dan minimum dari TIGA sumber.

Sumber penulisan dapat berupa buku maupun web/link di internet.

TUJUAN PENULISAN

Mahasiswa dapat menjelaskan tentang:

1. Logika Fuzzy dan fungsinya pada Sistem Pakar

2. Bagaimana fuzzy dapat diterapkan pada Sistem Pakar

3. Cara kerja sebuah aplikasi Sistem Pakar memanfaatkan Sistem fuzzy

BANTUAN REFERENCE

Searching by Google

Keyword: Fuzzy, Membership Function, Fuzzifikasi, Inferensi, Defuzzifikasi.

.

Combinatorial Prob.

Real-world Problem in Transportation System 
(Based on the data: 2010)

This is an interesting benchmark for combinatorial problem and it is applied for Vehicle Routing Problem.

All the data based on the real world problem and its collected in 2010 from shipping company.

Each vehicle has different in specification i.e. size, capacity, speed and maximum travel distance allowed.

Some constraints:

1. Total travel time of each route not exceed the maximum travel time allowed.

2. Total travel distance of each route not exceed the maximum travel distance allowed.

3. There are a condition where not every ship can be serve of ports (site-dependent problem).

4. A route has at least one fuel port (multi-depot VRP).

The results using "set partitioning heuristic" to solve the problem could be seen in Table below:

We have data:

1. The size, capacity and speed of each ship.

2. The distance between each port.

3. The number of passenger between each port.

We have result when the problem solved by:

1. Heuristic: Nearest Neighboor

2. General Genetic Algorithm

3. Hybrid Genetic Algorithm

Please send email for more information.

.

IBSI/X1/2010 - 6

Latihan menjawab soal (i)

Sumber: TUGAS 1 (SISTEM PAKAR)

1.   Apakah yang dimaksud dengan:

      a.  Artificial Intelligence (AI)

      b.  Expert System (ES)

2.   Jelaskan beberapa tujuan penggunaan AI (Sumber: Widiyono)

3.   Gambarkan struktur ES

4.   ES terdiri atas 2 bagian, sebutkan dan jelaskan fungsi kedua
      bagian tersebut (Sumber: Hendra Indrawan)

5.   Apa yang anda ketahui tentang:

      a.  User Interface

      b.  Knowledge Base

      c.  Inference Engine

      d.  Knowledge Acquestion

      e.  Working Memory

      f.  Explanation Fascility

6.   Sebutkan beberapa perbedaan system konvensional dan ES
      (Sumber: Yusuf Wijaya)

Keterangan:

Expert System (ES) = Sistem Pakar

Artificial Intelligence (AI) = Kepintaran Buatan

Sumber: TUGAS 2 (REPRESENTASI PENGETAHUAN)

1.   Apa yang dimaksud dengan representasi pengetahuan (ES)

2.   Apa yang anda ketahui tentang representasi pengetahuan
      dalam bentuk:

      a.  Frame

      b.  Semantic Network

      c.  Script

      d.  Logic

      e.  Rule Base

3.   Jelaskan kelebihan representasi pengetahuan yang dibuat
      dalam bentuk rule base
      (Sumber: Widiyono dan Hendra Indrawan)

4.   Gambarkan blok diagram: Struktur Rule Base
      (Sumber: Yusuf Wijaya)

5.   Jelaskan cara penerapan representasi pengetahuan yang dibuat
      dalam bentuk rule base pada kasus (pilih):

      -   Sistem kontrol pada rem mobil, atau

      -   Sistem pendeteksi kerusakan HP

IBSI/X/2010 - 21

IS: 2nd Assignment



Merepresentasikan pengetahuan untuk sistem pakar dapat dilakukan dengan beberapa cara, yang mungkin berbeda satu dengan lainnya; bergantung pada masalah yang akan diselesaikan dan dengan memperhitungkan kemudahan yang sudah ada.

2nd Assignment

Membuat makalah tentang cara merepresentasikan pengetahuan untuk sistem pakar.

Dikumpulkan pada october 29, 2010; dan dikoordinasi oleh ketua kelas mata kuliah sistem pakar.

KETERANGAN

Jumlah kata adalah 1000 - 1500, dan ditulis dalam bahasa Indonesia ataupun English Language, dengan TIDAK LUPA menyebutkan sumber/reference- nya.

Isi makalah mengandungi:

(seperti yang dicontohkan pada Merepresentasikan Pengetahuan pada Sistem Pakar )

1. Penjelasan singkat mengenai representasi pengetahuan dalam sistem pakar.
2. Menyebutkan sedikitnya lima jenis cara untuk merepresentasikan pengetahuan dalam sistem pakar.
3. Menjelaskan secara khusus representasi pengetahuan jenis rule-base.

Isi makalah HARUS memuat gambar dan penjelasan bagaimana cara merepresentasikan pengetahuan yang tersebut diatas.

HARUS menyebutkan sumber penulisan (reference) dan minimum dari TIGA sumber.

Sumber penulisan dapat berupa buku maupun web/link di internet.

TUJUAN PENULISAN

Dapat menjelaskan permasalahan pada representasi pengetahuan untuk sistem pakar.

Dapat menyebutkan serta menjelaskan jenis-jenis representasi pengetahuan dalam sistem pakar.

BANTUAN REFERENCE

Searching by Google
Keyword: Representasi Pengetahuan, Rule Base

IBSI/X/2010 - 15

Ibnu Sina: 1st Assignment (Result)

NIM: xx101xxxxxx01

Sepatutnya dalam makalah tidak mengandungi pembahasan mengenai "Sistem Pakar dalam Bidang Farmakologi dan Terapi". 1st assignment belum meminta contoh aplikasi.

Perhatikan cara penulisan referensi, seperti yang dicontohkan di:

http://ai-softcomp.blogspot.com/2010/09/ibnu-sina-1st-assignment.html

Perhatikan batasan penulisan, jumlah kata adalah 1000 - 1500.

Status: Dipertimbangkan untuk diterima

NIM: x91xxxxxxxx4x

Pembahasan dalam makalah yang dibuat adalah tentang aplikasi sistem pakar. Dan makalah ini adalah secara penuh diambil dari satu bahan sumber.

Penulis makalah ini, tidak mencantumkan gambar arsitektur dari ES sehingga penulis tidak dapat menjelaskan fungsi dan peranan masing-masing bahagian ES tersebut secara mendetail. Sedangkan tujuan utama dalam tugas ini adalah mahasiswa dapat:

- Menggambarkan arsitektur dari ES serta menyebutkan bahagian-bahagian nya.

- Dapat menjelaskan fungsi dan peranan masing-masing dari bahagian ES tersebut.

- Dapat membandingkan keragaman dari arsitektur ES.

Status: Gagal dan harus memasukkan makalah lain sesuai yang dijelaskan di:

http://ai-softcomp.blogspot.com/2010/09/ibnu-sina-1st-assignment.html

NIM: -

Nama: HI (inisial)

Perhatikan cara penulisan referensi, seperti yang dicontohkan di:

http://ai-softcomp.blogspot.com/2009/06/representasi-pengetahuan.html

Perbaiki format penulisan.

Status: Diterima

.

IBSI/IX/2010 - 30

1st Assignment:

Membuat makalah tentang arsitektur Sistem Pakar (ES).

Dikumpulkan pada october 14, 2010.

KETERANGAN

1. Arsitektur dan bahagian-bahagian dari expert system mungkin berbeda satu dengan lainnya bergantung pada sumber penulisan. Dan, ini DIBENARKAN sepanjang dapat menyebutkan sumber/reference- nya.
2. Jumlah kata adalah 1000 - 1500, dan ditulis dalam bahasa Indonesia ataupun English Language.
3. Isi makalah menjelaskan tentang bahagian-bahagian dari expert system, berdasarkan gambar yang dikemukakan.
4. Isi makalah HARUS memuat gambar arsitektur dari expert sistem (seperti yang dicontohkan pada http://ai-softcomp.blogspot.com/2007/06/structure-of-es.html).
5. Penjelasan tentang bahagian-bahagian dari expert system harus mengandungi: User interface, Knowledge Base, Inference Engine, Knowledge Acquisition, Working Memory and Explanation Facility Knowledge.
6. HARUS menyebutkan sumber penulisan (reference) dan minimum dari tiga sumber.
7. Sumber penulisan dapat berupa buku maupun web/link di internet.

TUJUAN PENULISAN

Dapat menggambarkan arsitektur dari ES serta menyebutkan bahagian-bahagian nya.

Dapat menjelaskan fungsi dan peranan masing-masing dari bahagian ES tersebut.

Dapat membandingkan keragaman dari arsitektur ES.

 

BANTUAN REFERENCE

Searching by http://scholar.google.com.my/

Keyword: Structure of Expert System, Arsitektur Sistem Pakar.

.

BOOK - FGA

B O O K

Authors: Ismail Yusuf, Yusram. SPd and Nur Iksan. ST

This book investigates the use of Genetic Algorithms (GA) to design and implement of Fuzzy Logic Controllers (FLC). A fuzzy logic is fully defined by its membership function. What is the best to determine the membership function is the first question that has be tackled. Thus it is important to select the accurate membership functions but these methods possess one common weakness where conventional FLC use membership function and control rules generated by human operator. The membership function selection process is done with trial and error and it runs step by step which is too long in solving the underlined the problem.

This book proposes a method that may help users to determine the membership function of FLC using the technique of GA optimization for the fastest processing in solving the problems.

The book discusses the literature review up to date from the concept of closed loop control system, fuzzy logic control, genetic algorithm and matlab.

Book Details
Book Title     The Application of Real Numbers Code for GA
Authors         Ismail Yusuf, Yusram Spd, and Nur Iksan ST
Publisher       Lap Lambert Academic, Germany.
ISBN-10       3838398491
ISBN-13       9783838398495
Edition          Paperback 08/2010
Total Pages   148
Language       English
Dimension     6 x 9.02 x 0.34 inches

Price
Rs. 3675 by nbcindia
CDN$ 73.69 by amazon.ca
$125.95 by booktopia
£48.00 by amazon.co.uk
266 zł by petlaczasu
$75.00 by amazon
R1,016.44 by kalahari
$75.00 by shopping@pedia

In future requests, please always add the ISBN (978-3-8383-9849-5) in your subject line.
This will speed up processing.

Thank a lot !

IBSI/IX/2010 - 23

Course Guide (3): AI   

Course Guide:
Introduction to Artificial Intelligence

Faculty Name: Information and Communication Technology
School Name: Sekolah Tinggi Teknik (STT) Ibnu Sina
Campus: Teuku Umar Rd, Lubuk Baja. Batam, KEPRI. Indonesia
Course: Artificial Intelligence (AI)
Credit Points: 3


Class = 10% (Minimum: 10times)
Discussion = 20 %
Assignments = 20 %
Midle Test = 20 %
Final Test = 30%

Assessment Criteria and Gradings Available

The following table lists the possible grades for the course, and the corresponding percentages that apply, as appropriate:
HD (80 - 100) = High Distinction
DI (70 - 79) = Distinction
CR (60 - 69) = Credit
PA (50 - 59) = Pass
NN (00 - 49) = Fail
DNS = Did not sit/did not submit
RW = Result withheld (see lecturer for reasons)

Planned Student Learning Experiences

Pokok Pembahasan: Pengantar tentang Sistem Pakar
Tujuan Intruksi Khusus: Mahasiswa mendapat gambaran tentang apa yang dimaksud dengan Sistem Pakar
Sub Pokok Pembahasan:
1. Defenisi
2. Kaitan dengan Al
3. Ruang Lingkup
4. Penerapan
5. Contoh Aplikasi

Pokok Pembahasan: Arsitektur Sistem Pakar
Tujuan Intruksi Khusus: Mahasiswa dapat menjelaskan bagian-bagian dari arsitektur Sistem Pakar dan keterkaitannya
Sub Pokok Pembahasan:
1. User interface
2. Knowledge Base
3. Inference Engine
4. Knowledge Acquisition
5. Working Memory
6. Explanation Facility Knowledge

Pokok Pembahasan: Representation
Tujuan Intruksi Khusus: Mahasiswa dapat menjelaskan bagaimana cara merepresentasikan pengetahuan untuk Sistem Pakar
Sub Pokok Pembahasan:
1. Permasalahan pada representasi pengetahuan untuk Sistem Pakar
2. Jenis-Jenis Representasi Pengetahuan
3. Frame
4. Semantic Network
5. Script
6. Logic
7. Rule Base

Pokok Pembahasan: Knowledge Representation (Lanjutan)
Tujuan Intruksi Khusus: Mahasiswa dapat menjelaskan kelebihan representasi pengetahuan jenis 'rule base'
Sub Pokok Pembahasan:
1. Prinsip Rule base
2. Kelebihan representasi menggunakan rule base
3. Contoh kasus penerapan rule base

Pokok Pembahasan: Inference Engine
Tujuan Intruksi Khusus: Mahasiswa dapat menjelaskan apa yang dimaksud dengan inference engine dan metodanya
Sub Pokok Pembahasan:
1. Kegunaan utama bagian Inference Engine
2. Teknik pemecahan masaiah dengan Tree/Graph
3. Metoda Deduktif dan Silogisme
4. Forward chaining backward Chaining

Pokok Pembahasan: Working memory dan contoh kasus penerapan foward dan backward chaining
Tujuan Intruksi Khusus: Mahasiswa dapat menganalisa metoda inferece engine baik penalaran dengan Forward maupun Backward dan bagaimana working memory berfungsi
Sub Pokok Pembahasan:
1. Perbedaan, kelebihan dan kelemahan masing-masing teknik forward dan backward
2. Fungsi working memory pada proses inference
3. Contoh kasus

Pokok Pembahasan: Masaiah Ketidak-pastian (Uncertainty)
Tujuan Intruksi Khusus: Mahasiswa dapat menjelaskan apa yang dimaksud dengan keadaan ketidak-pastian (Uncertainty)
Sub Pokok Pembahasan:
1. Apa yang dimaksud dengan ketidak-pastian (Uncertainty)
2. Kesalahan induksi
3. Probabilitas
4. Teorema Bayes

Pokok Pembahasan: Mengatasi masalah Ketidak-pastian
Tujuan Intruksi Khusus: Mahasiswa dapat menjelaskan bagaimana cara mengatasi masaiah ketidak pastian Sub Pokok Pembahasan:
1. Penerapan Probabilistik dan Teorema Bayes
2. Penalaran ketidakpastian
3. Faktor Kepastian (CF = Certainty Factor)
4. Studi Kasus

Pokok Pembahasan: Sistem Fuzzy pada Sistem Pakar
Tujuan Inruksi Khusus: Mahasiswa dapat menjelaskan tentang logika fuzzy dan fungsinya pada Sistem Pakar
Sub Pokok Pembahasan:
1. Sekilas tentang apa itu Logika Fuzzy
2. Dasar-dasar logika Fuzzy: Himpunan dan keanggotaan fuzzy, operasi dasar fuzzy, aturan IF-THEN

Pokok Pembahasan: Sistem Fuzzy pada Sistem Pakar (Lanjutan)
Tujuan Intruksi Khusus: Mahasiswa dapat menjelaskan pada masalah bagaimana fuzzy dapat diterapkan pada Sistem Pakar
Sub Pokok Pembahasan:
1. Bagaimana memfungsikan logika fuzzy pada Sistem Pakar
2. Proses penalaran fuzzy: Fuzzifikasi, Inferensi, Komposisi, Defuzzifikasi

Pokok Pembahasan: Sistem Pakar Berbasis Logika Fuzzy (Study Kasus).
Tujuan Intruksi Khusus: Mahasiswa dapat menjelaskan cara kerja sebuah aplikasi Sistem Pakar memanfaatkan sistem fuzzy
Sub Pokok Pembahasan:
1. Contoh Aplikasi Sistem Pakar berbasis Fuzzy
2. Arsitektur Sistem dan cara kerja
3. Analisa proses Fuzzy pada system

Pokok Pembahasan: Pengembangan Sistem Pakar
Tujuan Intruksi Khusus: Mahasiswa dapat merancang-bangun proyek Sistem pakar
Sub Pokok Pembahasan:
1. Tahapan Pengembangan Sistem Pakar
2. Kualifikasi orang-orang yang terlibat pada pengembangan Sistem Pakar
3. Contoh kasus dan latihan membangun sebuah proyek Sistem Pakar

Pokok Pembahasan: Tool untuk membangun Sistem Pakar
Tujuan Intruksi Khusus: Mahasiswa dapat menggunakan tool untuk membangun Sistem Pakar
Sub Pokok Pembahasan:
1. Pengenalan jenis-jenis tool untuk membangun Sistem Pakar
2. Prolog: mewakili pemrograman deklaratif
3. Clips: mewakili semacam Shell
4. Matlab: Mewakili Pemrograman prosedural dengan fungsi-fungsi komputasi soft-computing yang built-in

Pokok Pembahasan: Tool untuk membangun Sistem Pakar (Lanjutan)
Tujuan Intruksi Khusus: Mahasiswa dapat mempraktekkan tool untuk membangun Sistem Pakar
Sub Pokok Pembahasan:
Praktikum dan latihan menggunakan tool Expert System

Ujian Akhir Semester

Texts, References and Other Learning Resources

Title : A Guide to Intelligent Systems
Author(s) : Negnevitsky, M.

Title : The Application of Real Numbers Code for Genetic Algorithms
Author(s) : Ismail Yusuf, Yusram. SPd and Nur Iksan. ST

Other Reference : CliCK




.

Introduction: AI

Definition

Artificial intelligence (AI) is a part of computer science that studies how to make the machines (computers) can perform such work and is best done by human beings could even do better than humans.  AI is defined as intelligence exhibited by anything manufactured (i.e. artificial) by humans or other sentient beings or systems (should such things ever exist on Earth or elsewhere). It is usually hypothetically applied to general-purpose computers. The term is also used to refer to the field of scientific investigation into the plausibility of and approaches to creating such systems. It is commonly abbreviated as AI, and is also known as machine intelligence.

Human may be more sensible to solve all the problems in his live because have the knowledge and experience. The knowledge gained from the study, more shares owned by an individual knowledge and of course, expected to be more able to solve the problem. But knowing is not enough, human also are given a mind to make reasoning, reach conclusions based on the knowledge and experience they have. Without the ability to reason or people with lots of experience and knowledge won't able to solve the problem correctly. Furthermore, human with very good ability of reasoning but without providing sufficient knowledge and experience then they are unable to solve the problem very well.

AI will give some methods to provide computer with two basic parts so that the computer can be intelligent machine and should act like humans. Two basic parts necessary for the application of artificial intelligence:

1. Knowledge base is contains the facts, theories and relationships with one another.

2. Inference engine is the ability to conclusions based on knowledge

Figure 1.



FL: Domain

The ordinary (crisp) sets E and ΔE are called the universes of discourse for е and é, respectively (in other words, they are their domains). In practical applications, the universe of discourse is the some interval of real numbers. Note that, for convenience we will refer to an effective universe of discourse [α, β] where α and β are the points at which the outermost membership function saturate for input universes of discourse, or the points beyond which the outputs will not move for the output universe of discourse.

Figure. Input membership function with input values

For example, for the е universe of discourse in Figure, we have α = -600 and β = 600. However, the actual universe of discourse for both the input and output membership function is the set of real number. When we refer to effective universes of discourse, we will say that the width of the universe of discourse is β – α.

Genetic Algorithms

Genetic algorithms (GA) can be seen as an unusual kind of search strategy. In GA, there is a set of candidate solutions to a problem; typically this set is initially filled with random possible solutions, not necessarily all distinct. Each candidate is typically an ordered fixed-length array of values (called alleles) for attributes (genes). Each gene is regarded as atom in what follows; the set of alleles for that gene is the set of values that the gene can possibly take. Thus, in building GA for a specific problem the first task is to decide how to represent possible solutions.

Suppose we have thus decided on such a representation, GA usually proceeds in the following way:

1. Initialization: A set of candidate solutions is randomly generated. For example, if the problem is to maximize a function of x, y and z, the initial step may be to generate a collection of random triples if that is the chosen representation.

2. Iteration: through the following steps, until some termination criterion is met (such as no improvement in the best solution so far after some specified time, or until a solution has been found the fitness of which is better than a given adequate value).

The process alters the set repeatedly; each set is commonly called a generation.

1. Evaluation.

Using some predefined problem-specific measure of fitness, we evaluate every member of the current set to determine good a solution of problem can be. The measure is called the candidate’s fitness, and the idea is that fitter candidates are, in some way, closer to be one of the solutions we are seeking. However, GA do not require that fitness is a perfect measure of quality; they can, to some modest extent, tolerate a fitness measure in which the fitter of some pairs of candidates is also the poorer as a solution.

2. Selection.

Select pairs of candidates solutions forms the current generation to be used for breeding. This may be done entirely randomly, or stochastically based on fitness.

3. Breeding.

Produce new individuals by using genetic operators on the individuals chosen in the selection step. There are two main kinds of operators:

a. Crossover: A new individual is produced by recombining features of a pair of parents solutions.

b. Mutation: A new individual is produced by slightly altering an existing one.

4. Population update.

The set is altered, typically by choosing to remove some or all of the individuals in the existing generation (usually beginning with the least fit) and replacing these with individuals produced in the breeding step. The new population thus produced becomes the current generation.

Figure:  A basic genetic algorithm

GA are a class of stochastic search algorithms based on biological evolution. A basic GA can be represented as in Figure. GA applies the following major steps by Mitchell (1996) and Negnevitsky (2005):

Step 1: Represent the problem variable domain as a chromosome of a fixed length; choose the size of a chromosome population, the probability of crossover rate and the probability of mutation rate.

Step 2: Define a fitness function to measure the performance, or fitness, of an individual chromosome in the problem domain. The fitness function establishes the basis for selecting chromosomes that will be mated during reproduction.

Step 3: Randomly generate an initial population of chromosomes of population.

Step 4: Calculate the fitness of each individual chromosome.

Step 5: Select a pair of chromosomes for mating from the current population. Parents chromosomes are selected with a probability related to their fitness. Highly fit chromosomes have a higher probability of being selected for mating than less fit chromosomes.

Step 6: Create a pair of offspring chromosomes by applying the genetic operators - crossover and mutation.

Step 7: Place the created offspring chromosomes in the new population.

Step 8: Repeat Step 5 until the size of the new chromosomes population becomes equal to the size of the initial population.

Step 9: Replace the initial (parents) chromosomes population with the new (offspring) population.

Step 10: Go to Step 4, and repeat the process until the termination criterion is satisfied.




Reference
.

Fuzzy Logic

INTRODUCTION:

FUZZY LOGIC

Automatic control has played role in the advance of engineering and science. In addition to its extreme importance in robotic systems, and the same, automatic control has become an important and integral part of modern manufacturing and industrial processes. Automatic control is essential in such industrial operations as controlling pressure, temperature, humidity, viscosity, and flow in the process industries.

While modern control theory has been easy to practice (Ogata, 2008), FLC has been rapidly gaining popularity among practicing engineers. This increased popularity can be attributed to the fact that fuzzy logic provides a powerful vehicle that allows engineers to incorporate human reasoning in the control algorithm.

The designed controller used fuzzy logic control (FLC) implements human reasoning that been programmed into fuzzy logic language. In FLC, the dynamic behavior of a fuzzy system is characterized by a set of linguistic description rule based on expert knowledge. The expert knowledge is usually of the form:

IF (a set of conditions are satisfied, associated with fuzzy concepts or linguistic terms), THEN (a set of consequences can be inferred).

The idea of fuzzy sets is due to Dr. Lotfi A. Zadeh, who introduced a function that expressed the degree of belonging to a given set as a function taking values in the range 0 to 1 (Shancez et. al., 1997).



Reference
.

Closed Loop Control

Controller design entails constructing a controller to meet the specification. Often the first issue to address is whether to use open- or closed-loop control. In an open-loop control system the output is neither measured nor feedback for comparison with the input. In a closed loop control system the actuating error signal, that is the difference between the input signal and the feedback signal, is fed into the controller so as to reduce the error and bring the output of the system to a desired value (Boulet, 2006).

Closed loop control systems are often referred to a feedback control system. In practice, the term feedback control and closed loop control are used interchangeably. A system that maintains a prescribed relationship between the output and the reference input by comparing them using the difference as means of control is called a feedback control system (Phillips & Harbor, 2000) as shown graphically in Figure 1.

Figure. Feedback control system

In analyzing and designing control system, we must have a basis comparison of performance of various control system. The basis may be set up by specifying particular test input signals and by comparing the responses of various systems to these input signals, where to use these typical input signals for analyzing system characteristic may be determined by the form of the input that the system will be subjected to most frequently under normal operation; if a system is subjected to sudden disturbances, a step function of time may be a good test signal (Ogata, 2008).

Once a control system is designed on the basis of test signals, the performance of the system in response to actual inputs is generally satisfactory. The use of such test signals enables to compare the performance of all systems on the same basis. Performance of various control system can be analyzed by concentrating time response. The time response of a control system consists of two parts: the transient response and the steady – state response. By transient response, we mean the one which goes from the initial state to the final state. By steady – state response, we mean the manner in which the system output behaves as t- time approaches infinity.

Figure2. Transient and Steady-state response analyses

The transient response of a practical control system often exhibits damped oscillations before reaching steady state. In specifying the transient-response characteristics of a control system to a unit-step input, it is common to specify the following Boulet (2006), Phillips & Harbor (2000) and Ogata (2008):

• Delay time

• Rise time

• Peak time

• Maximum overshoot

• Settling time

These specifications are defined in what follows and are shown graphically in Figure 2.

1. Delay time: The delay time is the time required for the response to reach half of the final value in the very first time.

2. Rise time: The rise time is the time required for the response to rise from 0% to 100% of its final value.

3. Peak time: the peak time is the time required for the response to reach the first peak of the maximum overshoot.

4. Maximum (percent) overshoot: The maximum overshoot is the maximum peak value of the response curve measured from the unity. The amount of the maximum (percent) overshoot directly indicates the relative stability of the system.

5. Settling time: The settling time is the time required for the response curve to reach and stay within a range about the final value of size specified by absolute percentage of the final value, usually 2% or 5% (Ogata, 2008). The settling time is related to the largest constant time of the control system. Percentage error criterion to use may be determined from the objectives of the system design in question.