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Coordinated control of unmanned aerial vehicle (UAV)

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Title: Coordinated control of unmanned aerial vehicle (UAV)


1
Coordinated control of unmanned aerial vehicle
(UAV)
  • Presented by Urmila Prakash
  • Graduate Student
  • Electrical and Computer
    Engineering
  • Utah State University

2
References
  • An Intelligent Approach to Coordinated Control of
    Multiple Unmanned Aerial Vehicles-George
    Vachtsevanos, Liang Tang, Johan Reimann, School
    of Electrical and Computer Engineering, Georgia
    Institute of Technology, Atlanta, GA, 30332.
    U.S.A.
  • Coordinated control of unmanned aerial vehicle -
    Peter Joseph Seiler, Doctor of Philosophy
  • In Engineering-Mechanical Engineering in
    the GRADUATE DIVISION of the UNIVERSITY OF
    CALIFORNIA, BERKELEY, Fall 2001
  • Intelligent flying robots and wireless sensor
    networks in dynamic environment- H Jin Kim, EECS
    department, University of California, Berkeley,
    UKC 2004

3
Overview
  • Introduction to coordinated control
  • Architecture for coordinated control
  • Formation flight control problem

4
Introduction
  • Received significant attention in the controls
    community due to its numerous applications
  • Applications
  • Space science mission
  • Surveillance
  • Terrain Mapping
  • Formation flight
  • In these applications, unmanned vehicles are used
    because they can outperform human pilots, they
    remove humans from dangerous situations, or
    because they perform repetitive tasks that can be
    automated

5
Why Coordinated Control ?
  • The future urban warfare will utilize an
    unprecedented level of automation in which
    human-operated, autonomous, and semi -autonomous
    air and ground platforms will be linked through a
    coordinated control system.
  • Networked UAVs bring a new dimension to future
    combat systems that must include adaptable
    operational procedures, planning and
    deconfliction of assets coupled with the
    technology to realize such concepts.
  • The technical challenges the control designer is
    facing for autonomous collaborative operations
    stem from real-time sensing, computing and
    communications requirements, environmental and
    operational uncertainty, hostile threats and the
    emerging need for improved UAV and UAV team
    autonomy and reliability

6
Formation Flight
  • The problem of finding a control algorithm, which
    will ensure that multiple autonomous vehicles can
    maintain a formation while traversing a desired
    path and avoid intervehicle collisions, will be
    referred to as the formation control problem. The
    formation control problem has recently received
    considerable attention due in part to its wide
    range of applications in aerospace and robotics.
  • Moreover, formation flight itself has many
    applications.
  • For example, flying in formation can reduce fuel
    consumption by 30. However, this requires tight
    tracking to realize these fuel savings.
  • For airborne refueling and quick deployment of
    troops and vehicles
  • Cooperating vehicles may also perform tasks
    typically done by large, independent platforms.
    Gains in flexibility and reliability are
    envisioned by replacing large platforms with
    smaller vehicles operating in a formation.

7
Architecture
  • A novel architecture for the coordinated control
    of multiple UAVs acting as intelligent agents
  • A commander is placed at the highest level of
    the hierarchy. At the current level of autonomy,
    the system under development is acting as a
    decision support tool for the commander.
  • The architecture is generic and flexible to
    facilitate the fusion of diverse technologies.

8
A Generic Hierarchical Multi-agent System
Architecture Source An Intelligent Approach to
Coordinated Control of Multiple Unmanned Aerial
Vehicles- George Vachtsevanos, Liang Tang, Johan
Reimann,School of Electrical and Computer
Engineering,Georgia Institute of Technology,
Atlanta, GA, 30332. U.S.A
9
  • While networked and autonomous UAVs can be
    centrally controlled, this requires that each UAV
    communicates all the data from its sensors to a
    central location and receives all the control
    signals back. Network failures and communication
    delays are one of the main concerns in the design
    of cooperative control systems.
  • On the other hand, distributed intelligent agent
    systems provide an environment in which agents
    autonomously coordinate, cooperate, negotiate,
    make decisions and take actions to meet the
    objectives of a particular application or
    mission.
  • The autonomous nature of agents allows for
    efficient communication and processing among
    distributed resources.
  • For the purpose of coordinated control of
    multiple UAVs, each individual UAV in the team is
    considered as an agent with particular
    capabilities engaged in executing a portion of
    the mission.
  • The primary task of a typical team of UAVs is to
    execute faithfully and reliably a critical
    mission while satisfying local survivability
    conditions.

10
  • Consider two possible distributed control
    architectures
  • each vehicle could use a control law that
    depends on measurements from all vehicles in the
    formation. This architecture allows us to design
    centralized controllers but requires the vehicles
    to communicate large amounts of information.
  • distributed control architecture where each
    vehicle uses only sensor information about
    neighboring vehicles. This architecture does not
    require communication, but it may lead to
    disturbance propagation. Specially, disturbances
    acting on one vehicle will propagate and, if
    amplified, may have a large effect on another
    vehicle. This amplification of disturbances is
    commonly called string instability.

Source Coordinated control of unmanned aerial
vehicle - Peter Joseph Seiler, Doctor of
Philosophy In Engineering-Mechanical
Engineering in the GRADUATE DIVISION of the
UNIVERSITY OF CALIFORNIA, BERKELEY, Fall 2001
11
Formation Control Problem
  • The formation control problem is viewed as a
    Pursuit Game of n pursuers and n evaders.
    Stability of the formation of vehicles is
    guaranteed if the vehicles can reach their
    destinations within a specified time, assuming
    that the destination points are avoiding the
    vehicles in an optimal fashion.
  • Vehicle model is simplified to point mass with
    acceleration limit. Collision avoidance is
    achieved by designing the value function so that
    it ensures that the two vehicles move away from
    one another when they come too close to each one.

Source An Intelligent Approach to Coordinated
Control of Multiple Unmanned Aerial Vehicles-
George Vachtsevanos, Liang Tang, Johan
Reimann,School of Electrical and Computer
Engineering,Georgia Institute of Technology,
Atlanta, GA, 30332. U.S.A
12
  • The most natural way to represent the information
    topology is through directed graphs.
  • A directed graph consists of a set of vertices
    and a set of directed edges pointing from one
    vertex to another. The vertices represent the
    vehicles in the formation.
  • The communication channels and sensing
    capabilities generate the edges of the graph. In
    general, these edges may be directed or
    bidirectional depending on the capabilities of
    the vehicle

Source Coordinated control of unmanned aerial
vehicle - Peter Joseph Seiler, Doctor of
Philosophy In Engineering-Mechanical
Engineering in the GRADUATE DIVISION of the
UNIVERSITY OF CALIFORNIA, BERKELEY, Fall 2001
13
Left Blue Angels in Delta formation Right Graph
representing a possible information topology for
the Delta formation Source Coordinated control
of unmanned aerial vehicle - Peter Joseph Seiler,
Doctor of Philosophy In Engineering-Mechani
cal Engineering in the GRADUATE DIVISION of the
UNIVERSITY OF CALIFORNIA, BERKELEY, Fall 2001
14
  • Graphs are not only useful as a representation of
    the information topology, but also as a tool for
    control design. Give each edge of the graph a
    cost and find the optimal topology with respect
    to these costs using the Dijkstra algorithm.
  • The Laplacian matrix of a graph to state a
    Nyquist-like stability criterion for a formation
    .However, if the Laplacian condition shows a
    small stability margin, it is not clear if you
    need to change the information topology (keeping
    the controller fixed), change the controller
    (keeping the information topology fixed) or some
    combination of the two.
  • The use of combinatorial optimization over valid
    graphs as a tool for control synthesis.

15
Wireless Sensor Network
  • Whats a Sensor Network?
  • Its a network of devices (nodes)
  • Many nodes 1.000-100.000
  • Multi-hop wireless communication with adjacent
    nodes
  • Ad-hoc, i.e. dynamic and self-organizing
  • Suite of sensors
  • Temperature, Magnetometer,Chemical,
  • A small computer (CPU memory DSP)
  • Advantages
  • Large-scale fine-grain monitoring of the
    environment
  • Robustness
  • Inexpensive and disposable
  • Self-configurable
  • Easily deployable
  • Very small (targeting 1mm3 with Smart Dust)

Source Intelligent flying robots and wireless
sensor networks in dynamic environment- H Jin
Kim, EECS Department, University of California
Berkeley, UKC 2004
16
Source Intelligent flying robots and wireless
sensor networks in dynamic environment- H Jin
Kim, EECS Department, University of California
Berkeley, UKC 2004
17
Source Intelligent flying robots and wireless
sensor networks in dynamic environment- H Jin
Kim, EECS Department, University of California
Berkeley, UKC 2004
18
Source Intelligent flying robots and wireless
sensor networks in dynamic environment- H Jin
Kim, EECS Department, University of California
Berkeley, UKC 2004
19
Control Issues in Sensor Network
  • Packet loss and random delay
  • Bandwidth limitation
  • Quantization error and compression
  • Estimation and distributed signal reconstruction
  • Distributed tracking of multiple evaders
  • Coordinated control or multiple pursuers

20
Conclusion
  • By viewing the formation control problem as a
    differential game, important performance
    information about the formation can be
    determined, for example, the existence of
    solutions for any given set of initial
    conditions, the time to reach the target and
    whether a designated formation flight path is
    reachable.
  • Moreover, the analysis of one formation of
    vehicles cannot always be translated onto another
    formation with different dynamics.

Source An Intelligent Approach to Coordinated
Control of Multiple Unmanned Aerial Vehicles-
George Vachtsevanos, Liang Tang, Johan
Reimann,School of Electrical and Computer
Engineering,Georgia Institute of Technology,
Atlanta, GA, 30332. U.S.A
21
  • The errors are amplified as they propagate and
    hence these strategies are sensitive to
    disturbances. This motivated a control design
    procedure for formation flight that required
    communicated leader information.
  • We then determined how often this information
    must be communicated for acceptable control. The
    'how often' is determined by the sample rate of
    the system as well as the packet loss
    characteristics of the network.

Source Coordinated control of unmanned aerial
vehicle - Peter Joseph Seiler, Doctor of
Philosophy In Engineering-Mechanical
Engineering in the GRADUATE DIVISION of the
UNIVERSITY OF CALIFORNIA, BERKELEY, Fall 2001
22
Source Intelligent flying robots and wireless
sensor networks in dynamic environment- H Jin
Kim, EECS Department, University of California
Berkeley, UKC 2004
23
Future work
  • Investigate different distributed control
    architectures
  • Explore design aspects for networked control
    systems

24
Thank You
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