Comprehensive Report on Dynamic Modeling Techniques for UAV Systems

This report provides a comprehensive overview of dynamic modeling techniques applied to Unmanned Aerial Vehicles (UAVs). It begins with an introduction to UAV technology and its applications, including military, civilian, and humanitarian uses. The literature review covers the evolution of UAVs, focusing on Vertical Take-Off and Landing (VTOL) mini Air Vehicles (mAVs) and their increasing importance. The report then delves into the core concepts of dynamic modeling, emphasizing the importance of understanding and simulating UAV behavior for control system design and analysis. Key equations, including those for dynamic systems, motion, state space, and rotation matrices, are presented and explained. The report also touches upon the application of dynamic UAVs in humanitarian relief and the advantages of rotary wing UAVs. It references several studies on PID control, deep learning techniques for state estimation, and optimal control methods for UAV applications. The report concludes by highlighting the significance of dynamic modeling in enhancing UAV performance and stability. The report includes a bibliography of relevant research papers and articles.

Dynamic Model of UAV
Student name
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Abstract
1. Introduction
2. Literature Review
The use of Unmanned Aerial Vehicles began
in the early 90’s due to military development.
Originally, unmanned vehicles were designed
for use in places where humans could not reach.
Compared to manned crafts, UAVs were
initially used for operations that were too '
tedious, nasty or harmful for human beings,
whereas they emerged primarily in military
purposes, their use rapidly expanded to industry,
science, recreational, horticultural and solution
applications, such as police, peacemaking and
police investigations, deliveries of products,
aerial photography, trafficking and drones.
Civilian UAVs currently immensely add up
military UAVs, with estimates of over 1 million
sold-out by 2016.
Motivated by a desire to utilize UAVs in an
urban setting, involvement in Vertical Take-Off
and Landing (VTOL) mini Air Vehicles (mAVs)
has doubled dramatically in recent years.
Globally, many principles were implemented.
The Bidule mAV was built up at Sydney Uni to
examine small flight device design problems.
The beta version, the Bidule CSyRex, is a joint
venture in both Sydney College and Compiègne
School of Technology to create a Bidule VTOL
version (Guerrero & Lozano, 2012).
Dynamic modeling is an essential step in a
dynamic system's production and control.
Depending on different conditions, the approach
allows the engineer to evaluate the model, its
potential and its behavior. In addition, dynamic
models are commonly used in the design of
controls (Noth, Bouabdallah & Siegwart, 2019).
A sample model of UAV system include
that which was modelled and presented to
International Conference on Innovative
Computing Technology in 2017. The paper is
Focused on quadrotor modelling and regulation;
first moments and torque modeling; second rotor
modeling; the result with full UAV dynamics.
The conceptual model is presented for a general
study with disturbance and we take into
consideration all control parameters. The PID
control system is identified without disturbance
in a dynamic formula for a sequential system,
where we use it to control a group of quadrotors
to avoid collisions, and a group of quadrotors
with rigid and responsive URLs to supply
payloads in a free zone (Bravo & Leiras, 2015).
Application of Dynamic UAV’s have
increased, the recent development being
Humanitarian relief. Since 2001, unmanned
aerial vehicles (UAVs) or drones have been used
in humanitarian response following the 9/11
terror attack. UAV aerial imaging offers key
information for professional developers and
choice - producers to analyze and act effectively.
According to Meier (2014), in reaction to a
disaster, quite small and compact UAVs are
already used to take high-pixel density visuals,
the application of Dynamic UAV models will
son expand to micro transportation fields.
Google has manufactured and evaluated
autonomous aircrafts and believes they can be
used to transport goods (Kamel et al,2017).
Turboprop UAVs have much more strength
and endurance than Fixed wing UAVs, but for
landing and taking they need runways as well as
other liftoff frameworks. In this case, rotary
wing UAVs are more flexible and adaptable as
they don't need runways to land and take off.
Hey can navigate easily via tight areas and
indoors. Therefore, Rotary wing UAVs are
generally preferred due to extreme o their
flexibility (Tahir et al., 2016).
The main equations used in modelling
dynamic systems have been shown below
Dynamic systems are described by equations
of motions given below.

These equations will be explained below.
dx/dt = f(x,p),
The integral equations defines the Semi-Explicit
Form equation relating to motion
f(dx/dt,x,p)=0, Open Equation Form
Equations describing motion in relation to force
F = m a
The above equations represent the relation of
mass and acceleration with respect to force
acting on an object. The force applied is directly
proportional to mass and acceleration
dy/dt = v
the differentials of distance with relation to time
gives velocity of an object.
dv/dt = a (Al-Sharman et al., 2019)
The equation above describes the relation of
velocity with acceleration. The differential of
velocity with respect to time gives acceleration
from the equations above, we can derive all the
dynamic modelling equations.
state space equations (Al-Sharman et al., 2019)
The state space equations describe the behavior
of a dynamic system as a set of first order
ordinary differential equations (ODE).
The x,y and z describes the state vectors of the
motion.
The whole equation describes the state matrix of
the system model
rotation matrix equation for describing rotary
motions (Al-Sharman et al., 2019)
The equation above describes the rotation on all
axis of the system.
Roation on each axis that is, X,Y and Z are
described individually as shown below,

The matrixes above describe the individual
rotation on different axis. Combining all the
three rotation matrixes results to a general
rotation matrix as discuss above.
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