🎛 Training Plan: Control Engineer (AITL)
This document defines the role-based training plan for Control Engineers
working with systems based on AITL (Architecture for Integrated Technology Logic).
This plan is aligned with ISO 9001 Clause 7.2 (Competence) and focuses on
preserving real-time control responsibility while enabling supervised use
of adaptive and AI-assisted methods.
1️⃣ Role Definition
A Control Engineer under AITL is responsible for:
- ⚡ Designing and validating real-time control loops
- 🛡 Ensuring system stability, safety, and deterministic behavior
- 📐 Defining operating envelopes and safe modes
- 🤝 Cooperating with supervisory and design-level functions
without relinquishing control authority
🔑 Control Engineers are directly responsible for real-time system behavior.
2️⃣ Training Objectives
After completing this training, a Control Engineer shall be able to:
- Explain control behavior based on physical models and assumptions
- Design and tune PID-based control loops with clear stability justification
- Explain the role of FSMs in supervision and mode management
- Identify conditions under which adaptation must be limited or disabled
- Clearly explain why LLMs are excluded from real-time control
3️⃣ Required Training Layers
Control Engineers must complete training in the following AITL layers.
🧪 Layer 1: Physical and System Constraints
Required competence:
- Understand physical limits affecting controllability
- Identify constraints that cannot be compensated by control
- Recognize model breakdown conditions
🎯 Layer 2: Models and Real-Time Control
Required competence:
- Develop and validate control-oriented models
- Design and tune PID controllers
- Analyze stability, response, and robustness
Key rule:
- ⚡ Real-time control authority resides exclusively in PID and FSM
🔄 Layer 3: Supervisory Control (FSM)
Required competence:
- Design FSM-based supervision logic
- Define safe mode transitions
- Handle faults, degradation, and recovery
Key rule:
- 🧭 FSM determines when control modes may change
🔁 Layer 4: Adaptive Assistance (Bounded)
Required competence:
- Understand NN / RL as adaptive assist mechanisms
- Define explicit bounds and safety limits
- Recognize when adaptation threatens stability or responsibility
Key rule:
- 🔒 Adaptive methods may assist but must not override control logic
🧠 Layer 5: Design-Time Intelligence (LLM)
Required competence:
- Use LLMs for offline analysis and review only
- Interpret logs and trends without delegating decisions
- Communicate limitations of AI-assisted analysis
Key rule:
- 📘 LLMs are strictly non-real-time and non-authoritative
🧯 Layer 6: Boundary Confirmation and Recovery
Required competence:
- Detect loss of controllability
- Decide when to stop control action
- Escalate to design recovery processes
4️⃣ Learning Materials
Training materials shall be selected from the Samizo-AITL main repositories,
including:
- 📘 Control architecture documentation
- 🎛 PID / FSM PoC implementations
- 🧪 Control playground demonstrations
- 🧯 Envelope control and recovery concepts
🚫 This plan does not introduce alternative control frameworks.
5️⃣ Verification Criteria
Competence verification shall confirm that the Control Engineer can:
- Justify control designs using physical and control reasoning
- Explain FSM supervision and mode transitions
- Identify unsafe use of adaptive or AI-based methods
- Demonstrate awareness of responsibility boundaries
Verification methods may include:
- Design explanation
- Control response interpretation
- Scenario-based discussion
- Checklist-based assessment
6️⃣ Training Records
Completion of this training shall be recorded using:
- 📄 Templates defined in
03_Training_Record
Records must indicate:
- Training layers completed
- Verification outcome
- Verifier identity
7️⃣ Completion Criteria
A Control Engineer is considered trained under AITL when:
- All required layers are completed
- Verification criteria are met
- Real-time control responsibility is clearly understood and accepted
8️⃣ Summary
This training plan ensures that Control Engineers:
- ⚡ Retain full authority over real-time behavior
- 🤝 Use adaptive and AI-assisted methods responsibly
- 🛡 Protect system stability and safety
🔑 AITL-based control depends on accountable engineers, not autonomous intelligence.