Title page for ETD etd-04282009-102424


Type of Document Dissertation
Author Morel, Yannick
Author's Email Address ymorel@vt.edu
URN etd-04282009-102424
Title Applied Nonlinear Control of Unmanned Vehicles with Uncertain Dynamics
Degree PhD
Department Mechanical Engineering
Advisory Committee
Advisor Name Title
Leonessa, Alexander Committee Chair
Kurdila, Andrew J. Committee Member
Southward, Steve C. Committee Member
Stilwell, Daniel J. Committee Member
Woolsey, Craig A. Committee Member
Keywords
  • output feedback
  • adaptive control
  • nonlinear control
  • autonomous vehicles
  • collaborative control
  • control input saturation
  • nonlinear observers
Date of Defense 2009-04-17
Availability unrestricted
Abstract
The presented research concerns the control of unmanned vehicles. The results introduced

in this dissertation provide a solid control framework for a wide class of nonlinear uncertain

systems, with a special emphasis on issues related to implementation, such as control input

amplitude and rate saturation, or partial state measurements availability. More specifically,

an adaptive control framework, allowing to enforce amplitude and rate saturation of the

command, is developed. The motion control component of this framework, which works in

conjunction with a saturation algorithm, is then specialized to different types of vehicles.

Vertical take-off and landing aerial vehicles and a general class of autonomous marine vehicles

are considered. A nonlinear control algorithm addressing the tracking problem for a

class of underactuated, non-minimum phase marine vehicles is then introduced. This motion

controller is extended, using direct and indirect adaptive techniques, to handle parametric

uncertainties in the system model. Numerical simulations are used to illustrate the efficacy

of the algorithms. Next, the output feedback control problem is treated, for a large class of

nonlinear and uncertain systems. The proposed solution relies on a novel nonlinear observer

which uses output measurements and partial knowledge of the system’s dynamics to reconstruct

the entire state for a wide class of nonlinear systems. The observer is then extended

to operate in conjunction with a full state feedback control law and solve both the output

feedback control problem and the state observation problem simultaneously. The resulting

output feedback control algorithm is then adjusted to provide a high level of robustness to

both parametric and structural model uncertainties. Finally, in a natural extension of these

results from motion control of a single system to collaborative control of a group of vehicles,

a cooperative control framework addressing limited communication issues is introduced.

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