Modeling, Identification, and Control of an Unmanned Surface Vehicle

TLDR

The study aims to develop a planar motion model for an unmanned surface vehicle that balances richness and simplicity to support model‑based control design and trajectory optimization, and to implement and evaluate two trajectory‑tracking controllers. The authors model the USV using a speed‑scheduled multiple‑model approach on a modified rigid‑hull inflatable boat with automated throttle and steering, comparing several experimentally identified models across a wide speed range. Experimental analysis shows that a speed‑scheduled Nomoto first‑order steering model, augmented with a sideslip lag at low speeds, yields the best trade‑off, and that the backstepping controller significantly outperforms a PD cascade for variable‑speed, variable‑course trajectory tracking. © 2013 Wiley Periodicals, Inc.

Abstract

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.