Diagnostic-to-Control Data Pipeline

The data pipeline moves diagnostic signals to the controllers with bounded, predictable latency, the nervous system of real-time control.

The path from sensor to controller

A measurement travels a defined path: sensor, signal conditioning, digitizer, real-time network, and finally the estimator and controllers. Every stage adds latency, and for control what matters is that the total is bounded and predictable, not merely small on average. The pipeline is engineered for determinism first.

Real-time networking

Ordinary networks deliver data eventually; real-time control networks deliver it within a guaranteed time. Deterministic protocols, reserved bandwidth, and switches that bound queuing delay ensure that a measurement produced this cycle is available to the controller this cycle. Without this guarantee, the latency budget cannot be met.

Synchronization

Fusing many diagnostics requires knowing when each sample was taken. A shared clock, distributed to every node with tight precision, timestamps samples so the estimator can align them. Poor synchronization corrupts fused estimates, so timing distribution is a first-class part of the pipeline design.

Data volume versus control need

Diagnostics can produce far more data than the control loop can consume in real time. The pipeline splits paths: a reduced, low-latency stream for control and a full-rate stream archived for between-shot and offline analysis. Control gets only what it needs to act, fast; the rest is stored for later study.

In the Kronos program

Kronos designs the diagnostic pipeline for its machines so the fastest loops, above all vertical stabilization of the Hyperion breeder, receive their inputs within the tight latency budget those loops demand. The same synchronized data feeds the between-shot analysis and digital twin. The pipeline is specified and validated in simulation ahead of construction.