System identification-based control of an unmanned autonomous wind-propelled catamaran

Abstract

An autonomous catamaran, based on a modified Prindle-19 day-sailing catamaran and fitted with several sensors and actuators was built to test the viability of GPS-based system identification for precision control. Using an electric trolling motor for propulsion, and lead ballast to match all-up weight, several system identification passes were performed to excite system modes and model the dynamic response. The identification process used the Observer Kalman IDentification (OKID) method for identifying a linear time invariant plant model and associated pseudo-Kalman filter. System identification input was generated using a human pilot driving the catamaran on roughly straight line passes. A fourth order discrete time model was generated from the data, and showed excellent prediction results. Using these models, linear quadratic Gaussian (LQG) controllers were designed and tested with the electric trolling motor. These controllers demonstrated excellent line-tracking performance, with error standard deviations of less than 0.15 m. The wing-sail propulsion system was fitted, and these same controllers re-tested with the wing providing all propulsive thrust. Line-following performance and disturbance rejection were excellent, with the cross-track error standard deviations of approximately 0.30 m, in spite of wind speed variations of over 50% of nominal value.