Welcome To UTPedia

We would like to introduce you, the new knowledge repository product called UTPedia. The UTP Electronic and Digital Intellectual Asset. It stores digitized version of thesis, dissertation, final year project reports and past year examination questions.

Browse content of UTPedia using Year, Subject, Department and Author and Search for required document using Searching facilities included in UTPedia. UTPedia with full text are accessible for all registered users, whereas only the physical information and metadata can be retrieved by public users. UTPedia collaborating and connecting peoples with university’s intellectual works from anywhere.

Disclaimer - Universiti Teknologi PETRONAS shall not be liable for any loss or damage caused by the usage of any information obtained from this web site.Best viewed using Mozilla Firefox 3 or IE 7 with resolution 1024 x 768.



[img] PDF
Download (1488Kb)


A commonly used controller in the process industries is the Proportional-Integral- Derivative (PID) controller due to its features; cheap and easy to configured. Another type of controller that is now being developed is neuro-ruzzy logic controller that functions like a human brain which consists of interconnected processing elements called nodes or neurons that work together to produce an output function. The feature that it has that the PID controller does not have is the ability to be retrained to deal with various conditions in the process industries. A previous final year project on neuroruzzy logic controller yielded an unsatisfactory result as it could only perform for a single set point change [1]. The objective of this project is to improve the neuro-ruzzy logic controller so that it can control a process for a wider range of set point values. Data achieved for this project are through plant experiments using SIM 305 Pilot Plant: Plant 6 for the purpose of process modeling and computer simulation. Computer simulation is used to design the PID controller and the neuro-fuzzy logic controller. These two types of controllers are then compared and analyzed based on their performances. Controlled Variable (CV) Overshoot, Manipulated Variable (MV) Overshootand DecayRatio are the benchmarks used to compare and evaluate these two controllers. Based on the simulation results, the two controllers are in par since the benchmark values for the two controllers are nearly the same. However, it can be concluded at this stage that the PID controller is better than the neuro-fuzzy logic controller due to the smallest value of overshoot compared to the neuro-fuzzy logic controller.

Item Type: Final Year Project
Academic Subject : Academic Department - Electrical And Electronics - Power Electronics - Power Electronics - Control Devices
Subject: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Engineering > Electrical and Electronic
Depositing User: Users 2053 not found.
Date Deposited: 24 Oct 2013 09:48
Last Modified: 25 Jan 2017 09:45
URI: http://utpedia.utp.edu.my/id/eprint/9584

Actions (login required)

View Item View Item

Document Downloads

More statistics for this item...