Modeling, Identification, and Control of an Unmanned Surface Vehicle
Article first published online: 18 MAR 2013
© 2013 Wiley Periodicals, Inc.
Journal of Field Robotics
Volume 30, Issue 3, pages 371–398, May/June 2013
How to Cite
Sonnenburg, C. R. and Woolsey, C. A. (2013), Modeling, Identification, and Control of an Unmanned Surface Vehicle. J. Field Robotics, 30: 371–398. doi: 10.1002/rob.21452
- Issue published online: 2 APR 2013
- Article first published online: 18 MAR 2013
- Manuscript Accepted: 24 JAN 2013
- Manuscript Received: 14 JUN 2012
- U.S. Office of Naval Research. Grant Numbers: N00014-10-1-0185, N00014-11-1-0532
This paper describes planar motion modeling for an unmanned surface vehicle (USV), including a comparative evaluation of several experimentally identified models over a wide range of speeds and planing conditions. The modeling and identification objective is to determine a model that is sufficiently rich to enable effective model-based control design and trajectory optimization, sufficiently simple to allow parameter identification, and sufficiently general to describe a variety of hullforms and actuator configurations. We focus, however, on a specific platform: a modified rigid hull inflatable boat with automated throttle and steering. Analysis of experimental results for this vessel indicates that Nomoto's first-order steering model provides the best compromise between simplicity and fidelity at higher speeds. At low speeds, it is helpful to include a first-order lag model for sideslip. Accordingly, we adopt a multiple model approach in which the model structure and parameter values are scheduled based on the nominal forward speed. The speed-scheduled planar motion model may be used to generate dynamically feasible trajectories and to develop trajectory tracking control laws. The paper describes the development, analysis, and experimental implementation of two trajectory tracking control algorithms: a cascade of proportional-derivative controllers and a nonlinear controller obtained through backstepping. Experimental results indicate that the backstepping controller is much more effective at tracking trajectories with highly variable speed and course angle. © 2013 Wiley Periodicals, Inc.