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Linear Model Identification For Autonomous Sailboat Development | 18483
ISSN: 2155-9910

Journal of Marine Science: Research & Development
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Linear model identification for autonomous sailboat development

2nd International Conference on Oceanography

Shawn S Keshmiri, Katrina M Legursky and Richard Hale

Accepted Abstracts: J Marine Sci Res Dev

DOI: 10.4172/2155-9910.S1.008

Introduction: 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 model. Experimental set-up: 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 mainsail. 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.