Self-Oscillation based identification and heading control for Unmanned Surface Vehicles

The paper demonstrates the use of self-oscillation identification method for heading controller tuning of the autonomous unmanned surface vehicle (USV) Charlie. In short, the theory behind self-oscillation identification method is addressed and a model based controller design is described. Two controllers are implemented on the vehicle: controller with Euler backward differentiator for yaw rate calculation, Kalman filter based yaw rate estimator. The Kalman filter is also tuned on the basis of the identified model. The methodology for auto-tuning experiment has been described and implemented on the actual vehicle. The experimental results prove that the proposed method is easily implemented, non time-consuming and gives satisfactory results.