header
Image from OpenLibrary

Novel artificial bee colony techniques for optimal pid parameter tuning / Nasr Antar Mahmoud Elkhateeb ; Supervised Ragia Ismail Badr

By: Contributor(s): Material type: TextTextLanguage: English Publication details: Cairo : Nasr Antar Mahmoud Elkhateeb , 2017Description: 107 P. : charts , facsimiles ; 30cmOther title:
  • طرق جديدة معتمدة على مستعمرة النحل الاصطناعية للضبط الأمثل لمعاملات المتحكم التناسبى و التكاملى و الإشتقاقى [Added title page title]
Subject(s): Available additional physical forms:
  • Issued also as CD
Dissertation note: Thesis (Ph.D.) - Cairo University - Faculty of Engineering - Department of Electronics and Communications Summary: Swarm intelligence has proven its superiority as an evolutionary computational algorithm in solving real life application problems. Arti{uFB01}cial Bee Colony (ABC) is one of the most recent stochastic optimization algorithm based on the swarm intelligent behavior of honey bee swarm. The stochastic searching characteristic of the ABC leads to some limitations such as the impact of initial population, speed of convergence and limitation in large scaled optimization problems. This thesis proposed a novel real parameter searching methodology for ABC algorithm to overcome those limitations. The methodology is based on the relationship between ABC variants and the nature of its candidate solutions. The experimental results show that the proposed algorithm yield better performance when compared to the classic approaches such as the genetic algorithms (GAs) and the particle swarm optimization (PSO) in tuning optimal PID controllers for a real parameter system such as the robotic arm manipulator. Robotic systems have a complex non-linear dynamics and coupling relations which make accurate and robust control dif{uFB01}culties. The proposed optimization tuning approaches are able to {uFB01}nd an optimal control law without any need to derivatives and nonlinear control knowledge
Tags from this library: No tags from this library for this title. Log in to add tags.
Star ratings
    Average rating: 0.0 (0 votes)
Holdings
Item type Current library Home library Call number Copy number Status Date due Barcode
Thesis Thesis قاعة الرسائل الجامعية - الدور الاول المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.13.08.Ph.D.2017.Na.N (Browse shelf(Opens below)) Not for loan 01010110073618000
CD - Rom CD - Rom مخـــزن الرســائل الجـــامعية - البدروم المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.13.08.Ph.D.2017.Na.N (Browse shelf(Opens below)) 73618.CD Not for loan 01020110073618000

Thesis (Ph.D.) - Cairo University - Faculty of Engineering - Department of Electronics and Communications

Swarm intelligence has proven its superiority as an evolutionary computational algorithm in solving real life application problems. Arti{uFB01}cial Bee Colony (ABC) is one of the most recent stochastic optimization algorithm based on the swarm intelligent behavior of honey bee swarm. The stochastic searching characteristic of the ABC leads to some limitations such as the impact of initial population, speed of convergence and limitation in large scaled optimization problems. This thesis proposed a novel real parameter searching methodology for ABC algorithm to overcome those limitations. The methodology is based on the relationship between ABC variants and the nature of its candidate solutions. The experimental results show that the proposed algorithm yield better performance when compared to the classic approaches such as the genetic algorithms (GAs) and the particle swarm optimization (PSO) in tuning optimal PID controllers for a real parameter system such as the robotic arm manipulator. Robotic systems have a complex non-linear dynamics and coupling relations which make accurate and robust control dif{uFB01}culties. The proposed optimization tuning approaches are able to {uFB01}nd an optimal control law without any need to derivatives and nonlinear control knowledge

Issued also as CD

There are no comments on this title.

to post a comment.