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Optimal control
for quadrotors during inspection of power utility assets
A.
Ailon
2019/07/15
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Abstract
Renewable energy and power utilities inspection
by autonomous aircraft enables rapid and effective risk-free assessment
of the state of systems, and provides a qualitative and accurate assessment
of defects and damages. To realize maximum operational benefits of aerial
inspections, effective
controls for autonomous aircraft must be ensured and the system should
be operated in an optimal policy. Hence, the objective of this paper is
to consider optimal control strategies for a quadrotor
helicopter type UAV. Based on some structural properties of the considered
system, in particular the flatness property, we suggest a control strategy
that ensures tracking a time parameterized path
that connects two given points in the state space while minimizing the
energy consumption. The proposed controller enables longer-endurance missions
for the quadrotor and effective supervision and inspection of large energy
systems and power plants. In this paper the analysis of the control law
takes into consideration the highly nonlinear dynamic model of the quadrotor
and the electrical actuator model. The current approach in solving the
optimal control problem under consideration
allows one to eliminate the differential equation and to reformulate the
optimal control problem to a nonlinear dynamic programming problem. The
approach can make monitoring operations of renewable energy plants by
aerial vehicles, more efficient.
| Published in: Renewable Energy
& Power Quality Journal (RE&PQJ, Nº. 17) |
| Pages: 85-90 |
Date of Publication: 2019/07/15 |
| ISSN: 2172-038X |
Date of Current Version:2019/04/10 |
| REF: 229-19 |
Issue Date: July 2019 |
| DOI:10.24084/repqj17.229 |
Publisher: EA4EPQ |
Authors and affiliations
A. Ailon
Department of Electrical and Computer Engineering. Ben Gurion University
of the Negev, (Israel)
and School of Electrical Engineering
Key words
Aerial inspections for power utilities, quadrotors, nonlinear
systems, optimal control, minimum energy.
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