Automated Control and Intelligent Fallback System

Project Context

This project explores how we can leverage the efficiency and predictive power of artificial intelligence while preserving the hard safety guarantees required in critical systems. The application domain was a simulated industrial reactor control.

Approach

The system operates in two layers:

1. The Fast Path (AI): A neural network trained via reinforcement learning attempts to optimize the reactor parameters for maximum efficiency.
2. The Safe Path (Deterministic): A formally verified fallback controller that constantly monitors the system state and the AI's proposed actions.

graph TD
    A[Sensors] --> B(State Estimation)
    B --> C{AI Controller}
    B --> D[Safety Monitor]
    C --> D
    D -- Safe --> E[Actuators]
    D -- Unsafe --> F[Fallback Controller]
    F --> E

Results

We demonstrated that the AI controller could run freely to optimize resources by 15% on average, while the Safety Monitor triggered the deterministic fallback in < 0.1% of edge cases, completely preventing catastrophic failure modes.

Future Exploration

This dual-architecture maps perfectly to current research in "AI Alignment" for cyber-physical systems, which I intend to pursue further in a PhD context.