الفهرس 
Chapter 1:IntroductionOnly 14 pages are availabe for public view
 |  | from | 142 |  |  |
| | | from | 142 | | |
Abstract
The most important challenges in the Fourth Industrial Revolution (4IR) in
improving quality control of production lines are big data exchange, real-time
monitoring and optimization of industrial control systems. For example, data
exchange among devices is an essential component of operational processes in smart
factories to show the state of production, the rate of energy consumption, the lack of
materials, customer requests, and product quality. These challenges require
employing modern technologies such as Artificial Intelligence, Big Data analytics,
Machine-to-Machine (M2M) communication and IoT to be overcome.
This thesis tackles the problems of improving the quality control of industrial
components and tuning various controllers automatically and remotely.
More specifically, determining:
- an online model for self-tuning Proportional-Integral-Derivative (PID)
controller parameters in real time-domain.
- Stand-alone tunning model for Fractional order Proportional-Integral-
Derivative (FOPID) controller on AVR system based on Industrial Internet of
Things (IIoT) layers.
Solutions to these problems are based on developing, implementing and
incorporating swarm intelligence optimization algorithms to controllers and
enabling communication with the system using IIoT to be controlled remotely.
from this perspective, the main contributions of the thesis are:
• Proposed an architectural model for determining the online self-tuning PID
controller parameters using an enhanced version of Harris Hawks Optimization Algorithm (EHHOA) and IIoT. The simulation results of the
proposed algorithm are compared with various algorithms: classical Harris
Hawks Optimization (HHO)” Algorithms, Particle Swarm Optimization
(PSO) and Ziegler-Nichols (Z-N). The proposed algorithm gave satisfactory
results during the adjustment of the PID controller.
• Proposed an IIoT architectural model to replace the traditional strategy of
tunning based on FOPID parameters for real-time optimization in AVR
system by integrating customized Chaotic Whale Optimization Algorithm
(CCWOA) to automatically display the results and enable the remote control
to users.