A control cabinet with PLCs and industrial edge computers.

C · CONTROL

Physical AI Control

Connect AI prediction and optimization while preserving the authority of safety PLC and motion control.

01

PROBLEM

Factory challenge

Brownfield data is isolated and responsibility between AI and safety control is unclear.

02

APPROACH

OPTO approach

Use EdgeLink, PredictCare and TwinForge for protocol integration, anomaly signals and virtual FAT.

PERFORMANCE · target metrics

<5ms Control cycle Deterministic real-time
99.99% Safety availability Guaranteed Safe State
35% Less downtime Predictive maintenance
2-loop Separated accountability Safety ↔ intelligence

※ Figures are planning assumptions, agreed per sample and site conditions via PoC gates.

Control Loop

From sensing to virtual FAT — bounded intelligence over safety

It takes sensor and vision feedback to predict and recommend, while deterministic PLCs own safety control in a Dual-loop architecture.

  1. 01 · SENSE

    Sense (sensors · vision)

    Collect equipment state and process variables from sensors and vision feedback.

  2. 02 · PREDICT

    Predict (anomaly · wear)

    Anomaly-detection and predictive-maintenance models forecast failures and quality drift.

  3. 03 · DECIDE

    Decide (optimize · recommend)

    Optimize and recommend parameters under constraints (limited authority).

  4. 04 · ACT

    Bounded action

    Adaptive control only within the safety-PLC boundary.

  5. 05 · VERIFY

    Virtual FAT

    Pre-validate with SIL/HIL and synthetic data before field rollout.

SOLUTIONS

Representative solutions

Safe AI action and data access for brownfield equipment

A control cabinet connecting PLCs and industrial edge computers
ControlP0 · concept validation

EdgeLink

Brownfield protocol, data and AI gateway

Connects legacy PLCs, sensors and upper-level systems while keeping write authority, timing and safe-state responsibilities explicit.

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A technician inspecting industrial equipment sensors and an edge device
ControlConcept · validation required

PredictCare

Sensor-based anomaly detection and asset health

Detects changes in vibration, current, temperature and pressure signals and supports maintenance decisions with operating-context evidence.

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A demo lab validating manufacturing equipment and robot operations on digital displays
ControlConcept · validation required

TwinForge

SIL/HIL, synthetic data and virtual FAT

Validates control logic, robot missions and data interfaces in a virtual environment before they are introduced to the production line.

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Why our Physical AI

Bounded intelligence added on top of safety

AI never replaces the safety PLC. Deterministic control stays as-is while AI handles only prediction, optimization and recommendation — accountability kept separate.

  • AI predicts/recommends; PLC controls deterministically
  • Adaptation from vision & sensor feedback
  • Connects brownfield equipment data
  • Pre-validated by virtual FAT

Edge & Control Stack

Edge & control stack

Edge
AI-EdgeLinkReal-time inferenceProtocol conversionData buffering
Models · AI
Anomaly detectionPredictive maintenanceRL recommendationVision-guided control
Safety
Safety PLCDual-loop separationSafe StateAudit logs
Twin
SIL/HILSynthetic dataVirtual FATDigital twin

PROOF & DELIVERABLES

Verification deliverables that remain

Acceptance is based on the documents, data and procedures needed for approval and operation.

  1. 01I/O and protocol map
  2. 02Safety and control responsibility boundary
  3. 03SIL/HIL test package
  4. 04Alarm and anomaly criteria
  5. 05Change and recovery runbook

INTEGRATION

Integration scope

  • PLC and motion systems
  • Industrial sensors
  • MES and SCADA
  • ROS 2 and robots
  • Network and identity services

INDUSTRIES

Priority industries

CONTROL

Design a verification path for your site

Specifications and performance are agreed through sample and site-specific PoC gates.

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