Adaptive Integral Sliding Mode Control for Attitude Tracking of Underwater Robots With Large Range Pitch Variations in Confined Space

TL;DR

This paper introduces an adaptive integral sliding mode controller for underwater robots, enabling precise attitude tracking in confined spaces with large pitch variations and disturbances, outperforming traditional methods.

Contribution

It proposes a novel AISMC that adaptively adjusts gain and integrates an integral module, improving robustness and accuracy in complex underwater environments.

Findings

AISMC achieves better tracking accuracy than PID and SMC.

The controller reduces chattering and enhances robustness against disturbances.

Experimental results validate the effectiveness of AISMC in confined spaces.

Abstract

Underwater robots play a crucial role in exploring aquatic environments. The ability to flexibly adjust their attitudes is essential for underwater robots to effectively accomplish tasks in confined space. However, the highly coupled six degrees of freedom dynamics resulting from attitude changes and the complex turbulence within limited spatial areas present significant challenges. To address the problem of attitude control of underwater robots, this letter investigates large-range pitch angle tracking during station holding as well as simultaneous roll and yaw angle control to enable versatile attitude adjustments. Based on dynamic modeling, this letter proposes an adaptive integral sliding mode controller (AISMC) that integrates an integral module into traditional sliding mode control (SMC) and adaptively adjusts the switching gain for improved tracking accuracy, reduced chattering, and enhanced robustness. The stability of the closed-loop control system is established through Lyapunov analysis. Extensive experiments and comparison studies are conducted using a commercial remotely operated vehicle (ROV), the results of which demonstrate that AISMC achieves satisfactory performance in attitude tracking control in confined space with unknown disturbances, significantly outperforming both PID and SMC.