Edge AI & Cyber-Physical Automation

Fault-tolerant control at the edge

Bridge microcontroller neural inference, switched dynamical systems, and MQTT telemetry pipelines into mission-critical automation architectures with Lyapunov-stable control guarantees.

Microcontroller neural inference (TensorFlow Lite, ONNX Runtime)
Fault-tolerant PID/MPC control loops with observer design
Switched dynamical systems and hybrid automata modeling
MQTT / WebSocket telemetry streaming pipelines
Edge-to-cloud digital twin synchronization

Case Studies

Smart Factory Predictive Control

Industrial Automation OEM

The Challenge

Legacy PLC controllers could not adapt to non-linear process dynamics, causing 14% yield loss during mode transitions across 47 production lines.

Control & Agent Architecture

Edge MPC controller with Kalman observer running on industrial gateways. MQTT telemetry feeds a digital twin; switched system model handles mode transitions with stability certificates.

Hardware / Software Stack

STM32 / ESP32 microcontrollersMQTT (Eclipse Mosquitto)Python MPC solverGrafana / TimescaleDBFastAPI telemetry API

Measured ROI

11.3% yield improvement; $8.7M recovered annual revenue across deployed lines.

Case study · smart-factory-mpc

Distributed IoT Anomaly Detection

Energy Infrastructure Operator

The Challenge

2,400 remote sensor nodes generated alert fatigue with 73% false-positive rate, delaying response to genuine equipment failures.

Control & Agent Architecture

On-device TFLite classifiers with federated threshold tuning. Fault-tolerant control loop switches between nominal and safe-mode controllers based on Lyapunov stability margins computed at the edge.

Hardware / Software Stack

TensorFlow Lite MicroMQTT over TLSEdge TPU inferenceSupabase time-seriesReact telemetry dashboards

Measured ROI

False positives reduced to 8%; mean-time-to-detect improved from 4.2h to 11 minutes.

Case study · iot-anomaly-edge