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ABSTRACT

 

System control has grown from manual control where humans sense the environment,
analyse the result and determine control actions to semi-automated control. In semi-automated
control sensing is done by sensing devices and with the help of communication technologies,
humans at a control centre view the environment, analyse it and determine control action.
However, advances in computational and processing technologies has now shifted the most
important role of making control decisions away from humans, to
microcontrollers/microprocessors. The problem now is how to make these devices intelligent
enough to make the best control decision. Different control methods have been used in system
control they include, the proportion integral derivative (PID) control, Model-Based control and
control based on Artificial Intelligence (AI). We proposed an architecture and explored
technologies that can be used to realize Discrete Event System Specification (DEVS)-Based
Control, which is a Model-Based approach. The proposed approach is based on a simulation
model rather than the optimization model used in other Model-Based system control
approaches. Using DEVS in system control, we can model physical systems to be controlled,
run a fast simulation of the system based on the current state of the system and determine
control actions. Two-fold Communication issues were identified and the research addressed
different methods of solving the two-fold communication problems.

 

TABLE OF CONTENTS

 

INTRODUCTION 1
1.1 Context ………………………………………………………………………………………………………….. 1
1.2 Research Objectives …………………………………………………………………………………………. 3
1.3 Approach Adopted …………………………………………………………………………………………… 3
1.4 Organization of Work ……………………………………………………………………………………….. 3
2.0 LITERATURE REVIEW 4
2.1 System Control ……………………………………………………………………………………………….. 4
2.1.1 Conventional PID Control ……………………………………………………………………………………. 5
2.1.2 Model-Based Control ………………………………………………………………………………………….. 6
2.1.3 Artificial Intelligent Based Control ………………………………………………………………………… 7
2.2 Traffic System Control ………………………………………………………………………………………. 8
2.3 DEVS-Based System Control ………………………………………………………………………………. 9
3.0 SYSTEM ARCHITECTURE AND COMMUNICATION TECHNOLOGIES 11
3.1 Model-Based System Control Architecture …………………………………………………………. 11
3.2 SURVEY OF TECHNOLOGY FOR DEVS-BASED SYSTEM CONTROL ……………………………… 12
3.2.1 Communication Between the Physical System and the Computer System ……………….. 13
3.2.1.1 Radio Frequency Technology …………………………………………………………………………………. 14
3.2.1.2 GSM Technology ………………………………………………………………………………………………….. 17
3.2.1.3 Internet Protocol (IP) Network ………………………………………………………………………………. 18
3.3 Summary of Communication Technologies …………………………………………………………. 19
4.0 COMMUNICATION INSIDE THE COMPUTER 21
4.1 DISCRETE EVENT SYSTEM SPECIFICATION (DEVS) …………………………………………………. 21
4.1.1 Atomic DEVS Model ………………………………………………………………………………………….. 22
4.1.2 Coupled DEVS Model ………………………………………………………………………………………… 23
4.1.3 DEVS SimStudio Simulation Package …………………………………………………………………… 24
4.2 Communication Between the Computer Ports and The DEVS Model ………………………. 24
4.2.1 Serial/Parallel Port Communication API ………………………………………………………………. 25
4.2.1.1 Java Communication API (javax.comm) …………………………………………………………………… 25
4.2.1.2 RxTx Communication API (gnu.io.SerialPort) ……………………………………………………………. 25
4.2.1.3 Java Universal Serial Buss (USB) API (javax.usb) ……………………………………………………….. 26
4.2.1.4 Java Network API (java.net) …………………………………………………………………………………… 26
4.3 Summary of Java API for Communication …………………………………………………………… 27

 

CHAPTER ONE

 

1.0 INTRODUCTION
This chapter presents an overview of the research context, the motivation behind this research
and the corresponding aims and objectives. It begins with a brief introduction of modeling and
simulation, i.e. Discrete Event System Specification (DEVS). This is a formalism for discrete
event modeling and simulation, thereafter system control is introduced before presenting how
it has evolved. Moreover, it provides information about the motivation for the research and the
drive which steered the research goals. It is followed by a description of the research aims and
objectives, then the approach adopted is presented. Finally, the outline of the rest of the thesis
is revealed.
1.1 Context
Computational science (modeling and simulation) has become the third pillar of science along
side theory and physical experiment (PITAC, 2005). Modeling and simulation (M&S) enable
researchers to build and test models of complex real life systems. They do so without
conducting physical experiments, or building and testing models of phenomena that cannot be
replicated in the laboratory/physically. According to “The Theory of Modeling and
Simulation” (Zeigler et al., 2000), there are four major important concepts of M&S. The
concepts and relationship between them are shown in fig 1.
Figure 1.1: Basic entities in M&S and their relationships
a. Source System: Is a well-defined real or virtual environment that we are interested in.
This system can be viewed as a source of observable data or a database of system
behaviours.
Model
Source
System
Experimental Frame
Behaviour
Database
Model
Relation
Simulation
Relation
Simulator
2
b. Experimental Frame (EF): Is a specification of the condition under which the source
system is observed or experimented with.
c. Model: Is any physical, mathematical or logical representation of a system. However,
in M&S, a model is seen as a set of instructions, rules, equations or constraints used to
generate input/output behaviours.
d. Simulator: Is any computational system (single processor, processor network, human
mind or an algorithm) capable of executing a model to generate its behaviour.
For a complex dynamic system to be modeled, a robust formalism is needed. DEVS, a universal
discrete event specification language (Zeigler, 1976) introduced by Zeigler in the early 70’s is
a theoretically well-defined formalism for modeling discrete event systems in a hierarchical
and modular manner. In DEVS, each of the M&S components are well separated and a formal
mechanism is used to describe each of the components. This modularity makes DEVS most
suitable for the modeling of complex dynamic systems.
System Control is an application area of control theory, which is multidisciplinary in nature.
Researchers began to clearly understand its principle in the year 1922; this was after a clear
analysis of the control involved in position control was presented by Nicholas Minorsky
(Benneth, 1996). System control has evolved from analogue to digital controls, from
conventional to model-based to artificial intelligent based controls. In all forms of system
control, a model of the physical system is either implicitly or explicitly used to determine the
control input (Baskar et al., 2011). Most of the models are analytical models that require
analytical solving, but solutions for some of these models (especially for non-linear systems)
do not exist. To also note is that solving complex systems incurs a very high computational
cost or is impossible. To overcome this, a simulation model, which always have a solution, is
used.
Since DEVS can be used to model any physical system (discrete and continuous), and is a
robust formalism with direct relation to a simulator, using DEVS model in system control is
very much possible (Zeigler et al., 2000). We propose to use DEVS as the intelligence of
physical devices. The DEVS model is used to generate the physical system’s behaviour, and
this information is remotely communicated to the physical system, using appropriate
communication technology, while the model equally remotely gets feedback from the system.
SimStudio, a Java-implemented DEVS simulator, will be used for the behaviour generation
(simulation), while wireless communication will be used for the remote communication.
3
1.2 Research Objectives
The objective of this work is to propose an architecture for DEVS-Based System Control. We
aim to define an architecture that establishes a remote connection between DEVS-model
running on a computer and a physical scene. The physical scene receives commands from the
computer and sends feedback to the computer. Survey of possible technologies to achieve
DEVS-Based System Control was presented.
1.3 Approach Adopted
The approach taken in this thesis is to lay the foundation for the realization of DEVS-Based
System Control. Communication between different components of the system, which is a
fundamental problem to realizing the system control, is addressed. Survey of different
technologies to solve this problem is presented.
1.4 Organization of Work
This work is organized as follows: in Chapter 2, we review different control methods:
conventional control, Model-based control, intelligent control and DEVS-Based System
Control. Chapter 3 presents different technologies that can be used to achieve communications
in DEVS-Based System Control. Chapter 4 presents DEVS; DEVS Atomic Model, DEVS
Coupled Model and DEVS Simulator (SimStudio) implemented in Java and Application
Programming Interface (API) that can be used by DEVS model to communicate with the
computer ports. Chapter 5 provides the summary of the work done and future works.

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