LASSA Architecture-Based Autonomous Fault-Tolerant Control of Unmanned Underwater Vehicles

TL;DR

This paper introduces the LASSA architecture, an autonomous fault-tolerant control system for UUVs that leverages LLMs for fault detection and replanning, ensuring safe operation in communication-limited environments.

Contribution

It presents a novel LASSA framework that combines LLM reasoning with physical constraint verification for robust, interpretable fault-tolerant control of underwater vehicles.

Findings

Successfully detected and corrected faults in UUV trajectory tracking.

Replanned routes with adjusted turning radius and speed to maintain mission objectives.

Passed all solver constraints on first attempt, ensuring safety and reliability.

Abstract

Unmanned underwater vehicles (UUVs) operate persistently in communication-constrained environments, thus requiring high-level autonomous fault-tolerant control under faulty operating conditions. Existing approaches rely heavily on predefined hard-coded rules and struggle to achieve effective fault-tolerant control against unforeseen faults. Although large language models (LLMs) possess powerful cognitive and reasoning capabilities, their inherent hallucinations remain a major obstacle to their application in UUV control systems. This paper proposes an intelligent control method based on the LASSA (LLM-based Agent with Solver, Sensor and Actuator) architecture. Within this architecture, an LLM identifies unknown faults and accomplishes task replanning via autonomous reasoning without hard-coded rules; the intelligent agent undertakes perception, scheduling and decision evaluation; the solver verifies physical boundary feasibility constraints prior to command transmission to the actuators. This architecture suppresses physically infeasible LLM hallucinations and ensures interpretable, verifiable decision-making. Moreover, it enables fast-slow dual closed-loop collaborative control, where the slow loop undertakes high-level dynamic decision-making and the fast loop guarantees high-frequency real-time control, simultaneously balancing decision intelligence and control timeliness. Lake experiments under normal and lower-rudder-fault conditions show that the framework detects trajectory tracking abnormalities, replans the route by adjusting the turning radius from 4m to 12m and reducing speed from 2kn to 1kn, passes all three solver constraints on the first invocation, and guides the UUV to complete the full mission; under normal conditions no false fault alarms are raised throughout the run.