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Professor has given a reference paper .based on which i have submitted a project proposal and he gave the feedback. we need to use MPC controller matlab .we need to submit the final project report in...

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Model Predictive Control of an Autonomous
Underwater Vehicle for Cable/Pipeline Tracking
Lakshmi Kothuru∗,
ID: XXXXXXXXXX∗
Carleton University ∗, Ottawa, Canada
Email: XXXXXXXXXX ,
Abstract—The inspection and monitoring of sub-sea cables
and pipelines have become critical concerns in the modern world
as any damage in sub-sea cables affects the internet which plays
a pivotal role in everyday life. We will be using MPC (model
control predictive control) to generate reference headings fo
autonomous underwater vehicles (AUVs) and minimize the e
o
etween vehicle path and pipeline. ROVs (Remotely operated
vehicles) have limitations in their range of operation due to the
length of their tether, and the need for a support vessel and
operator increases the cost of monitoring operations. To address
these issues, making the vehicle autonomous would allow it to
perform tasks with minimal human intervention.
Keywords—model control predictive control;autonomous under-
water vehicles;Remotely operated vehicles; Application; Software
I. INTRODUCTION
Model predicative control (MPC) algorithms are designed
to optimize the future behavior of a plant by computing a
sequence of manipulated variable adjustments. While initially
developed for power plants and refineries, this technology has
found applications in a variety of fields such as chemicals,
food processing, automotive, aerospace, metallurgy, and more
[1].
II. OBJECTIVE
The primary objective of this project is to develop an
AUV capable of tracking underwater cables and pipelines
using Model Predictive Control [2]. The project aims to
design, simulate the results for the system that can operate
autonomously and achieve accurate tracking of underwate
cables and pipelines.In [3] The objective function is
J =
Hp∑
i=1
[ŷ(k + i)− w(k + i)]2
The components of the AUV velocity in the (x, y) plane can
e stated as,
• Vx = Vpcos(y)p
• Vy = Vpsin(y)p
III. PROPOSED SCHEDULE
S.No Activity Timeline
1
Conduct a literature review and
analyze existing systems and
technologies for cable/pipeline
15 Fe
uary, 2023
to
10 March, 2023
2 MPC MATLAB code and analysis
11 March, 2023
to
5 April, 2023
3 Documentation and Report
6 April, 2023
to
12 April, 2023
TABLE I. TIMELINE FOR THE PROJECT
REFERENCES
[1] H. W. Y. Shi, C. Shen and K. Zhang, Advanced Model Predictive Control
for Autonomous Marine Vehicles, Springer Nature. ISBN XXXXXXXXXX-
19353-8: In Press, 2023.
[2] A. S. M. Naeem W., Sutton R and B. R. S., “A review of guidance laws
applicable to unmanned underwater vehicles,” The Journal of Navigation,
vol. 56, no. 1, pp. 15–29, 2023.
[3] W. Naeem, R. Sutton, and S. Ahmad, “Pure pursuit guidance and model
predictive control of an autonomous underwater vehicle for cable/pipeline
tracking,” in Proceedings-Institute of Marine Engineering Science and
Technology Part C Journal of Marine Science and Environment. Cite-
seer, 2004, pp. 25–35.
Feedback 9/1
1. You have to explain clearly what the output y is in the cost function. What is w(k)? Are there any constraints?
2. In the model, you have to define what Vp, y, and p are.
3. The reference [2] was published in 2003.
Please note the final report should
e in a standard research pape
format. The IEEE conference
manuscript template can be found
here.
requirements), and proposed
approaches (should be MPC or at
least optimization-based approach)
and timeline.

Microsoft Word - IMarEST.doc
1
Pure Pursuit Guidance and Model Predictive Control of an
Autonomous Underwater Vehicle for Cable/Pipeline Tracking
W. Naeem, R. Sutton and S. M. Ahmad
{wnaeem, rsutton, sahmad}@plymouth.ac.uk
Marine and Industrial Dynamic Analysis Research Group
Department of Mechanical and Marine Engineering
The University of Plymouth, Plymouth, PL4 8AA, UK
Abstract
This paper investigates a new approach for the guidance and control of an
autonomous underwater vehicle (AUV). An integrated system is developed and
simulated involving a proportional navigation guidance (PNG) law and model
predictive control (MPC). The classical PNG law for missile systems has been
tailored to guide the AUV by generating reference headings. MPC is used to track the
eference trajectory (guidance commands), which is optimised using a genetic
algorithm (GA). The performance of the closed loop system is evaluated in
simulations with and without sea cu
ent distu
ance and imposing actuato
constraints. Simulation results for the case of a cable tracking mission and waypoint
following clearly shows the superiority of the proposed algorithm.
1. INTRODUCTION
The technology and applications of unmanned underwater vehicles (UUVs) have been
improving at a rapid pace. From missions such as cable/pipeline inspection to oil
exploration and to mine clearing operations, they are routinely been deployed by the
offshore and defence industry. This is mainly attributed to the fact that it does not
equire any human onboard thereby not jeopardizing any life. In addition, in cases
such as deep-sea exploration, where human intervention is not possible, they are
proved to be a viable tool. Although regular monitoring and inspection of
cables/pipelines running in deep sea have emerged as an important issue, little
attention has been paid to sub-sea cables or pipelines. This paper describes a novel
approach to underwater vehicle cable tracking mission by employing an integrated
guidance and control system using a PNG law for missile systems and MPC. The
contemporary method to detect linear subsea objects is through active magnetic,
2
passive magnetic or electromagnetic detectors mounted on a remotely operated
vehicle (ROV) [1]. These sensors provide lateral and longitudinal displacement of the
ROV from the target pipeline, but no target direction. Additional sensor is needed to
measure the target orientation. This information is then used by the ROV pilot to stee
the vehicle over the pipeline. Although ROVs have been employed for detection and
tracking, their range of operation is constrained by the length of the tether.
Furthermore, the need for a support vessel and an ROV operator adds to the cost of
monitoring operation. One way to circumvent these problems is to render the vehicle
autonomous, that is, they execute the task with minimal human intervention.
A variety of methodologies and concepts have been reported to perform object
tracking by an underwater vehicle. An account of various AUV guidance schemes has
ecently been documented by Naeem et al. [2] while a comparison of classical and
advance control strategies has been reported by Craven et al. [3]. In this paper, a
modified PNG law is proposed for tracking underwater cables/pipelines employing a
sonar system. MPC is used to track the reference commands generated by the PNG.
The intent is to demonstrate the suitability of the integrated guidance and control
scheme for detecting and tracking an undersea object, in this instance a pipeline, via
simulation. The tracking of a pipeline by an AUV is first posed as an AUV-target
interception problem. The classical PNG law is then employed to generate the
guidance command signals to the AUV. Subsequently this is modified to achieve the
desired target tracking trajectory objective.
1.1 Sonars
Recent advances in sonar technology provides a sophisticated means of finding fi
e
optic cable, plastic, metal and other materials suspended in mid-ocean or buried in a
seabed [4]. This strategy entails use of an active sonar system for target (pipeline)
detection. Active sonars employ echo ranging to detect an object whereas passive
sonars pick-up acoustic radiation of ships, submarines etc, by an a
ay of
hydrophones. Some of the several other factors that influence this choice are:
1. Active sonars echo-range and therefore are capable of detecting even a submerged
pipeline in the background of clutter i.e., reve
erations, in which it appears.
Vision based systems will have severe limitations in such a scenario which is very
3
likely to occur at sea bed due to underwater cu
ent and various other natural
distu
ances.
2. They can provide both range and orientation of the target, unlike magnetometers,
which are non-directional and can easily mislead the AUV in presence of subsea
fe
ous deposits.
3. Presence of onboard active sonar can also be employed for retrieval of an AUV
ack to the mother ship once mission is accomplished. This has been investigated
y Ahmad et al. [5] and is an area of ongoing research.
4. Sonic signals are the only practical and efficient way of long-range undersea
communication, for instance between the mother ship and the AUV [6].
The
oader aim of the authors is to render a underwater vehicle truly autonomous,
incorporating features such as smart launch, mid-course guidance, target tracking,
area search and finally, return and dock to the mother ship autonomously on
completion of a given task.
2. PROBLEM DEFINITION
The following assumptions are made in order to formulate the guidance problem:
i) The AUV-target engagement is planar i.e. in the same plane.
ii) Although the pipeline is a continuous object, it is convenient to assume it as a
point mass moving with a constant velocity. This condition can be ensured by
considering only the latest value of echoed ping received by an onboard AUV
sonar. The AUV is also considered as a constant velocity mass point.
iii) Complete navigational information of the target is available to the AUV.
Consider a two-dimensional engagement geometry in which the AUV and target are
closing on each other at constant velocities pV and eV respectively as shown in
Figure 1. An imaginary line joining the AUV and target is refe
ed as the line of sight
(LOS). The angle formed by the LOS with the fixed reference is λ and from the
geometry is given as,
h1tan −=λ (1)
4
where, h and r are the relative separation between the AUV and target perpendicula
and parallel to the fixed reference respectively. The relative movement between the
AUV and target causes the LOS to rotate through a small angle λ , indicating a
displacement h between AUV and target perpendicular to the fixed reference. The
length of LOS is a range R and represents the initial AUV-target distance. The
problem is then to develop an integrated system which will make the initial range R
etween the AUV and target as small as possible at the end of expected intercept time.
It will be shown later in simulation that it is a good starting point for achieving the
desired tracking objective, without actually intercepting the target.
3. GUIDANCE AND CONTROL
Herein a PNG law is utilised to obtain the guidance commands. The guidance
subsystem takes input from the sensors onboard the AUV. The sensors used could be
global positioning system (GPS) for positioning on the surface, inertial navigation
system (INS), compass etc. Information from the sensors is fused together and
provided to the guidance system, which then generate commands to be followed by
the AUV. A simple block diagram of the navigation, guidance and control system is
depicted in Figure 2. MPC is used to track the reference commands from the guidance
system. The selection of MPC for this paper is attributed to several factors, the most
important being its ability to handle constraints in a natural and systematic way. The
following subsections describe the PNG and MPC algorithms and their development.
3.1 Proportional navigation guidance law
The ultimate objective of the guidance law is to steer the AUV so that it will chase a
target using a constant AUV velocity pV and a controllable heading angle pψ .
However, initially it will be regarded as an AUV-target interception problem and then
subsequently modified to realise the desired “tail-chase” type AUV trajectory. The
tail-chase type trajectory of interest is akin to that formed when a dog is chasing a cat.
This type of trajectory will ensure that the AUV is always trailing behind the target
and thus continuously monitor it at a close length. From the discussion of Section 2, it
is intuitive that if the AUV is made to lie on the LOS and hold it there as well, a
constant relative bearing between the AUV and target is ensured that is, the LOS of
5
sight does not rotate, and interception will occur. This mechanisation can be realised
using a PNG law.
Proportional navigation is a method of guidance, which generates command signals
cu , proportional to the LOS angle λ , so that the pursuing vehicle remains on the
LOS. This can be mathematically stated as:
λ∝cu (2)
λkuc = (3)
Where, k is called the navigation constant and is an important design parameter. A
judicious choice of k will ensure that the LOS does not rotate and hence no furthe
input command is required. Thus, it influences both, the engagement trajectory as
well as the command input. The proportional navigation guidance scheme is
illustrated in Figure 3 and a good description on PNG can be found in [7].
3.1.1 Guidance law application
For implementing the guidance law of Equation 3, it is necessary to compute the LOS
angle λ . This requires relative positions of the AUV and target in both the co-
ordinates i.e.,
h = ye - yp (4)
= xe - xp (5)
therefore,








= −
pe
pe1
x- x
y - y
tanλ (6)
The components of the AUV velocity in the ),( yx plane can be stated as,
ppx cosVV ψ= (7)
ppy sinVV ψ= (8)
6
Hence, the differential equation for the components of the AUV position can be
expressed as:
xp Vx =& (9)
yp Vy =& (10)
It is assumed that the AUV speed pV and heading angle pψ are available to the
guidance logic from an onboard speed log and gyro compass respectively. In certain
cases both components of the AUV speed i.e., Equations 9 and 10 can be obtained
Answered 12 days After Apr 11, 2023

Solution

Aditi answered on Apr 15 2023
25 Votes
Paper Title (use style: paper title)
Inspection and Monitoring of Sub-Sea Cables and Pipelines using Model Predictive Control for Autonomous Underwater Vehicles
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Abstract- The inspection and monitoring of sub-sea cables and pipelines have become critical concerns in the modern world. This paper proposes the use of Model Predictive Control (MPC) to generate reference headings for Autonomous Underwater Vehicles (AUVs) to minimize the e
or between vehicle path and pipeline. Remotely Operated Vehicles (ROVs) have limitations in their range of operation, and the need for a support vessel and operator increases the cost of monitoring operations. Therefore, making the vehicle autonomous would allow it to perform tasks with minimal human intervention. The primary objective of this project is to develop an AUV capable of tracking underwater cables and pipelines using Model Predictive Control. This paper presents the proposed schedule and the components of the AUV velocity in the (x, y) plane.
Keywords: Model Predictive Control, Autonomous Underwater Vehicles, Remotely Operated Vehicles, Application,
Introduction
The inspection and monitoring of sub-sea cables...
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