Skip to content
Koidra
Book a demo

Autonomous control

KoPilot

Closed-loop control of the process, minute by minute, with no human at the panel.

Energy per tonne and yield stop depending on who is on shift, and the best night your plant has ever run becomes every night.

How it works

The closed loop, and the limits it obeys The process is read by a physics-aware model, whose proposed setpoints pass through an operating box that states the limits it may not cross, then reach the controllers, which write them back to the process. The loop runs every minute. The process dryer, boiler, hall, crop Physics-aware model knows what the equipment can and cannot do Operating limits set by your engineers Controllers setpoints written back every minute, not every shift
Every setpoint passes through limits your engineers set for your site. The model cannot learn or change them, and your safety systems and interlocks stay in charge.

What it takes over from

  • Setpoints tuned by hand, differently on every shift
  • Comfort margins nobody has revisited

Pays for itself by

Energy per tonne, yield, and consistency that no longer depend on who is on shift.

The number a buyer checks: Energy cost per unit of output, and output variance between shifts.

Proof

2×
Winner, Autonomous Greenhouse Challenge 2018 and 2022, a live crop each time
17%
Higher net profit than the expert growers 2018 competition, with 6% higher yield
19.5%
Yield increase on cucumber 29.7 against 24.9 kg/m² in the growers' own zone
17%
Energy savings lower electricity cost per unit, same lights in both zones

KoPilot has been tested in international competition and in commercial greenhouses. In 2018, the technology behind it was the only AI entry to beat the expert growers on both yield and net profit. KoPilot does more than recommend a climate strategy. It makes the control decisions and applies them, day and night.

What KoPilot does

KoPilot continuously adjusts process setpoints to keep operations close to the desired operating point. It reads real-time plant data, determines the appropriate setpoints, and sends those setpoints back to the existing control system.

Instead of waiting for an operator to review conditions and make the next adjustment, KoPilot responds continuously as conditions change.

How KoPilot runs a greenhousePlant and climate sensors in the greenhouse feed the climate computer, which sends the greenhouse data to KoPilot. KoPilot reads the weather forecast directly, and the grower adds crop registration, the strategy and the limits. KoPilot decides the growing conditions day and night and sends setpoints back to the climate computer, which moves the screens, vents, heating, lighting and CO2.greenhouse datadataforecastcrop registration, strategyand limitssetpointsPlant and climate sensorsWeather forecastGrowerClimate computerKoPilot, 24/7Screens, vents,heating, lighting, CO2
The loop in a greenhouse, where KoPilot started. The climate computer stays in charge of the equipment; KoPilot decides what it should be holding, and the operator sets the strategy and the limits.

Physics-informed control

KoPilot combines process data with models of how the physical system behaves, so a control decision stays within what the equipment can actually do rather than only within patterns seen in historical data.

How quickly equipment can heat or cool
A dryer cannot heat faster than its mass allows, so the model never proposes it.
How humidity responds to ventilation
Opening a vent changes humidity and temperature together, and the model knows by how much.
How one control action affects conditions later
A move that looks good this minute is rejected when it costs more over the next four hours.

Historical data remains useful, but autonomous control also has to behave sensibly when the process enters conditions it has rarely seen before.

Why the model is built from physicsA decision is checked against how the equipment behaves, how fast it heats or cools, how humidity answers ventilation, what one action does later, so it stays inside what the plant can do, even in conditions the history never showed.Process datawhat is happening nowKoPilot's modelPhysics of the planthow fast it heats or cools,how humidity answers ventilation,what an action does laterA setpoint the equipment can reacheven in conditions never seen before
The physics is the guard rail. It is what lets the model be trusted in a week the history never showed.

Start with the right foundation

Closed-loop control is not appropriate for every process on day one.

It depends on the quality and coverage of existing instrumentation, the reliability of the underlying control system, how well the process can be modeled, the operating constraints that must be respected, and whether a supervised trial can be run safely before autonomous operation.

Most customers start with Maintenance Management or DataPilot, and add Predix before moving to closed-loop control. Those products create the data, operational history and process understanding needed to make autonomous control more reliable.

Tell us about your process and we can assess where autonomous control is practical, what should come first, and how a supervised deployment could be structured.

What has to be true before closed-loop controlInstrumentation coverage, a reliable control system, a process that can be modelled, defined constraints and a safe supervised trial. Most customers build these with Maintenance Management or DataPilot, then Predix, before KoPilot.Maintenance Management or DataPilotthen PredixFive things to have firstinstrumentation that covers the processa reliable control systema process that can be modelleddefined operating constraintsa place for a supervised trialSupervised trialKoPilot, closed loop
The products before KoPilot are not a queue. They are where the data and the process understanding that make a closed loop safe come from.

Where this sits

You can start with Maintenance Management or with DataPilot. Predix needs both.

  1. Start here

    Maintenance Management

    Work orders, preventive maintenance, asset records and spare parts, on the phone your technicians already carry.

  2. Start here

    DataPilot

    Every signal the plant produces, in one place, live and historical.

  3. AI insights

    Predix

    Reports that say what changed and what to do next, and early warning of a failing machine.

  4. Autonomous control

    KoPilot

    Closed-loop control of the process, minute by minute, with no human at the panel.

See Koidra on your own plant data

Bring a week of readings from one line. We will show you what the platform sees, what it would have flagged, and what it would have scheduled.

Book a demo