Full-control optimization within AIVV for autonomous controller redesign

Develop a full-control optimization procedure for the REMUS 100 Unmanned Underwater Vehicle yaw-control system within the AIVV framework that integrates the System Engineer’s gain‑tuning proposals to autonomously optimize the PID controller (Kp, Ti, Td) and third‑order low‑pass filter parameters while maintaining verification‑and‑validation safety constraints.

Background

The paper introduces AIVV, a neuro‑symbolic framework that combines a mathematical anomaly detection engine with a multi‑agent LLM council to perform verification and validation, including proposing controller gain‑tuning actions. In the appendix, the authors implement sample gain‑tuning proposals on a REMUS 100 Simulink model and show improvements in yaw response, demonstrating feasibility of agent‑generated tuning.

Despite these gains, the authors explicitly state that achieving full‑control optimization is not yet solved. They frame this as an open engineering challenge, signaling that closing the loop between agent‑proposed tuning and end‑to‑end autonomous optimization—under V&V constraints—remains unresolved.