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🔁 Recovery Control Design

🎯 Role in This Package

Recovery Control Design is the final step of the
AI Control Safety Package.

Its role is to answer one critical question:

❓ When things go wrong, how do we return safely — and who decides?

This design step ensures that abnormal behavior does not lead to:

• ❌ Cascading failure
• ❌ Silent dependence on AI
• ❌ Undefined or ambiguous responsibility

Recovery is treated as a designed, governed process,
not as an emergency improvisation.

🧠 Overview

Recovery Control Design defines how a control system returns to a:

• 🧯 Safe
• 🧱 Stable
• 🧭 Explainable

state after abnormal behavior, disturbances, degradation,
or unexpected operating conditions.

The objective is not automatic optimization.

The objective is:

Controlled, deterministic recovery with clear responsibility

Recovery is treated as a first-class operational mode,
not as an exception.

🏗️ Conceptual Recovery Flow

Recovery is explicit and supervised

Recovery behavior is governed by explicit supervisory logic,
fully independent of AI judgment.

stateDiagram-v2
    Normal --> Recovery : Trigger detected
    Recovery --> Stabilizing : Baseline active
    Stabilizing --> Hold : Stability confirmed
    Hold --> Normal : Re-entry approved

Design intent

• ⚠️ Any trigger forces exit from Normal operation
• 🚫 AI has no authority during Recovery
• 🔒 Return to Normal is never automatic
• 🧭 Explicit approval is required for re-entry

🧩 What Is Recovery Control

Recovery Control is the explicit design of:

• ⏱️ When the system is considered degraded or abnormal
• 🧯 How normal operation is suspended or limited
• 🛣️ Which path the system follows to reach a safe baseline
• 👤 Who decides when recovery is complete

Recovery always prioritizes:

Safety · Predictability · Accountability

over continuity or performance.

🔍 Design Scope

1️⃣ Recovery Triggers

Explicit conditions that force entry into Recovery Mode:

• Detection of abnormal behavior or envelope violation
• Performance degradation beyond allowed limits
• Loss of confidence in AI-assisted decisions
• External or manual recovery requests

Triggers are explicit, conservative, and reviewable.

2️⃣ Recovery Modes

• Dedicated Recovery Mode clearly separated from Normal operation
• Reduced authority and simplified control structure
• Complete separation from AI-assisted or adaptive modes

Recovery modes are:

Finite · Deterministic · Explainable

3️⃣ Fallback & Baseline Control

• Transition to fixed, well-understood baseline controllers
• Guaranteed operability without AI involvement
• Known-safe behavior with bounded performance

Baseline control is a design guarantee,
not a failure or degraded feature.

4️⃣ Controlled Re-Entry

• Explicit criteria for exiting Recovery Mode
• Optional, bounded re-enablement of AI functions
• Human approval or supervisory confirmation when required

Re-entry is:

🔒 Explicit
🚫 Never automatic

🧪 Example: Recovery Control for AI-Assisted Process Control

(Example details are intentionally omitted.)

Examples are used only to validate recovery logic
—not to justify AI usage.

🚫 What This Design Does NOT Do

This design explicitly avoids:

• Unrestricted self-healing
• AI-driven recovery completion
• Silent failure masking
• Guarantees of uninterrupted performance

Recovery favors:

Clarity over continuity

📦 Deliverables

You will receive:

• 🔔 Recovery trigger definitions
• 🔁 Recovery mode structure and FSM transitions
• 🧱 Fallback and baseline control definition
• 🧭 Controlled re-entry criteria
• 📄 A Recovery Control design summary (PDF or Markdown)

🧑‍💼 Typical Use Cases

• Long-term operation with degradation or drift
• Systems requiring reversible AI involvement
• Preparation for abnormal or off-nominal events
• Accountability after unexpected system behavior

💼 Engagement Details

⚠️ Important Note

A system that cannot recover deterministically
is not suitable for AI-assisted control.

If safe recovery cannot be explicitly defined,
AI involvement must be:

🚫 Restricted or removed

📌 Previous step:
→ Safety Envelope Design

📌 Back to package overview:
→ AI Control Safety Package

📬 Contact

📧 shinichi.samizo2@gmail.com
🌐 samizo-aitl.github.io