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Control Systems for AI-Driven Physical Operations

7 chapters · 32 lessons

1. 1. Control Loop Architecture for Physical AI

   5 lessons

   Understand the sense-perceive-plan-control-actuate pattern and how AI fits into closed-loop systems.

2. 2. Sensor Integration and Data Quality

   4 lessons

   Handle real-world sensor data including noise, latency, drift, and missing observations.

3. 3. Safety Boundaries and Validation Gates

   5 lessons

   Define what AI may control directly, when human approval is required, and how to test before deployment.

4. 4. Integrating AI with Industrial Systems

   4 lessons

   Connect AI decision-making to legacy equipment, compliance requirements, and existing workflows.

5. 5. Deployment Strategies for Physical AI

   5 lessons

   Plan staged rollouts, monitoring, and rollback procedures that preserve operational control.

6. 6. Model Selection and Multi-Model Systems

   5 lessons

   Combine vision, time-series, language, and planning models to meet operational requirements.

7. 7. Case Studies Across Operational Domains

   4 lessons

   Apply control loop principles to manufacturing, logistics, mobile machines, and regulated environments.