Journal of Marine Science: Research & Development
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Sailing yachts have great potential to act as future long-term oceanic observing platforms, yet to date there have
not been complete autonomous sailing systems robust enough to handle long term operation in the harsh ocean environment.
The basis of control system design is a model capable of describing and capturing the necessary behavior of the system to
be controlled. A common method employed in the aerospace industry when designing autonomous autopilots is parameter
estimation where a physics-based model dependent upon motion variables, control inputs, and aerodynamic parameters is
derived and then full-scale measurements are taken and used to estimate the aerodynamic parameters. This work presents
results of using one-shot least squares estimation to fit a five degree of freedom linear state space sailing yacht model dynamic
A Precision 23 sailboat, Avanti, has been outfitted with an adequate sensor system to do parameter
estimation. The sensors provide boat speed through the water, apparent wind speed, wind angle, GPS position, velocity,
heading, rudder angle, sail angle, Euler angles (roll/heel, pitch, and heading) and rates, and accelerations. Data is collected by
a Labview Virtual Instrument (VI) PC interface which uniformly records and timestamps the data. Sailing tests are performed
on Clinton Lake in Lawrence, KS. In addition steady state sailing, the data includes maneuvers designed to characterize the
dynamic response of the vehicle to individual inputs, including rudder inputs, changes in sail angles, and responses to wind
gust and wind shift. The data analyzed here is that of the boat in the medium wind range with the jib foresail and the full
One-Shot Least squares estimation for linear models:
The LS algorithm may be solved in one step for linear systems, thus,
computationally it goes very fast and can be applied to several large sets of data and yield estimated models very quickly.
The estimated models are simulated in open loop with independent test data, and the fit is evaluated using R2 and the Theil
inequality coefficient, UT. A total of five models have an overall fit with R2 > 0.92 and UT < 0.17. These models will be
presented and discussed in this paper.
Shawn S Keshmiri, Assistant Professor. He joined the Department of Aerospace Engineering, University of Kansas in August of 2008. He received his BS in
Mechanical Engineering from College of Engineering, Shiraz University in 1993. After obtaining over five years of industrial experience in energy systems, he
attended California State University Los Angeles and worked as a researcher in the Multidisciplinary Flight Dynamics and Control Laboratory where he received
his MS in Mechanical Engineering in 2004. He received his PhD in Aerospace Engineering, from the University of Kansas in 2007. After graduation, he worked for
the University of Kansas as a postdoctoral fellow. He worked for the NSF Center for Remote Sensing of Ice Sheets (CReSIS), where he continues to do research.
He has also done research for NASA and the U.S. Air Force. He has established a large research team of 1 Postdoc, 5 PhD, and 9 MS students, with 22 graduate
and undergraduate students funded under his research projects. His research is diversified in the areas of modeling, simulation, and control of Unmanned Aerial
Systems (UASs) and autonomous surface vehicles (ASV). He is the National President of Sigma Gamma Tau which is the American honor society in Aerospace Engineering.
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