An Improved ESO-Based Line-of-Sight Guidance Law for Path Following of Underactuated Autonomous Underwater Helicopter With Nonlinear Tracking Differentiator and Anti-saturation Controller

TL;DR

The paper addresses the challenge of robust path following for a high-mobility, underactuated AUH by improving ESO-based LOS guidance. It introduces IELOS, which combines a reduced-order ESO with a nonlinear tracking differentiator and an anti-saturation controller to accurately estimate the time-varying sideslip angle $\beta$ under low bandwidth, and designs a guidance law $\psi_d = \alpha_k(w) + \arctan(-y_e/\Delta - \beta_{\text{sat}})$ with $u_p = U\cos(\psi_d-\alpha_k) + \kappa x_e$, ensuring ISS of the closed-loop. The approach is validated in simulation and pool experiments, showing lower cross- and along-track errors and reduced propeller torque compared with LOS, ALOS, and ELOS, particularly under low observer bandwidth. The results demonstrate improved sideslip tracking, buffeting mitigation, and reliable curved-path following for AUHs, supporting practical deployment and future field tests.

Abstract

This paper presents an Improved Extended-state-observer based Line-of-Sight (IELOS) guidance law for path following of underactuated Autonomous Underwater helicopter (AUH) utilizing a nonlinear tracking differentiator and anti-saturation controller. Due to the high mobility of the AUH, the classical reduced-order Extended-State-Observer (ESO) struggles to accurately track the sideslip angle, especially when rapid variation occurs. By incorporating the nonlinear tracking differentiator and anti-saturation controller, the IELOS guidance law can precisely track sideslip angle and mitigate propeller thrust buffet compared to the classical Extended-state-observer based Line-of-Sight (ELOS) guidance law. The performance of ESO is significantly influenced by the bandwidth, with the Improved Extended-State-Observer (IESO) proving effective at low bandwidths where the classical ESO falls short. The paper establishes the input-to-state stability of the closed-loop system. Subsequently, simulation and pool experimental results are showcased to validate the effectiveness of the IELOS guidance law, which outperforms both the Line-of-Sight (LOS) and Adaptive Line-of-Sight (ALOS) guidance laws in terms of performance.

Figures (12)

• Figure 1: Coordinate system
• Figure 2: Diagram of the IELOS
• Figure 3: Controller system of AUH
• Figure 4: The trajectory of various guidance laws in simulation
• Figure 5: The $y_e$ of various guidance laws in simulation