Trajectory-Tracking and Path-Following of Underactuated Autonomous Vehicles With Parametric Modeling Uncertainty

TLDR

The desired trajectory can be any sufficiently smooth bounded curve parameterized by time, without requiring a specific type such as a trimming trajectory. The study addresses position trajectory‑tracking and path‑following control for underactuated autonomous vehicles under potentially large parametric modeling uncertainty. The authors combine adaptive switching supervisory control with a nonlinear Lyapunov‑based tracking law for general 2‑ or 3‑D underactuated vehicles, demonstrating global boundedness and convergence of the tracking error and illustrating the design on a hovercraft and an underwater vehicle. The method enables path‑following without a specific temporal specification, and simulations confirm the theoretical results.

Abstract

We address the problem of position trajectory-tracking and path-following control design for underactuated autonomous vehicles in the presence of possibly large modeling parametric uncertainty. For a general class of vehicles moving in either 2- or 3-D space, we demonstrate how adaptive switching supervisory control can be combined with a nonlinear Lyapunov-based tracking control law to solve the problem of global boundedness and convergence of the position tracking error to a neighborhood of the origin that can be made arbitrarily small. The desired trajectory does not need to be of a particular type (e.g., trimming trajectories) and can be any sufficiently smooth bounded curve parameterized by time. We also show how these results can be applied to solve the path-following problem, in which the vehicle is required to converge to and follow a path, without a specific temporal specification. We illustrate our design procedures through two vehicle control applications: a hovercraft (moving on a planar surface) and an underwater vehicle (moving in 3-D space). Simulations results are presented and discussed.