Spiro MPC

Improve Process Performance

Spiro MPC is a multivariable model predictive control application that runs on the Spiro Control edge device platform. The control application automates responses to process disturbances in a way that optimizes plant operation.


  • Higher performance

    • Explicit disturbance model and Kalman filter provide optimal state estimation and disturbance rejection
    • An infinite horizon constrained dynamic move plan provides the optimal solution for the control task
    • Highly optimized code provides sub-second execution frequencies which can provide better control performance

  • Lower cost of ownership

    • The small form factor device upon which Spiro MPC is embedded provides an out-of-the-box solution that can be easily integrated into any industrial control or data network
    • Adaptive model capability means the MPC model can adapt in real-time as process conditions change
    • Integrated performance monitoring tool maintains performance benefits

  • Wider applicability

    • By using highly optimized code with fast execution times, and providing more robust implementation that is better integrated with the existing control hardware our solution can be targeted at a wider range of applications such as boilers, fired heaters and compressors.
    • Support for wide range of communication protocols – including OPC-UA, Modbus, Fieldbus, HTTP and Rest.

Basics of Model Predictive Control

Were it not for noise and disturbances, running a process at a constant optimum level would be relatively easy. You could set up a simple controller to do it. In reality though, we live in a chaotic world, where uncontrollable influences constantly disrupt the running of a plant.

Model predictive control improves the management of these process disturbances by executing multiple coordinated control moves many times a minute, continuously during production. This constant fine-tuning could not be achieved with a PLC and would be impossible for an operator to replicate.

The improved operational stability achieved by Spiro MPC facilitates increased production, lower energy costs, improved product quality, and better operational reliability.


Key Features

Adaptive control 2

Adaptive Model Capability

The embedded model is able to adapt to changes in the plant to deliver sustained benefits

cooperative distributed control

Cooperative Distributed Control

This facilitates significant efficiency gains by optimizing how process operations interact



Highly scalable modular architecture from single unit to multi-unit control

Communication protocols

Communication Protocols

High-speed data ingestion via OPC-UA, Fieldbus, Modbus and other protocols

IEC 61131-3

Highly Interoperable

Industry standard IEC 61131-3 compliant

Soft sensor

Virtual Sensor Capability

This feature replicates the function of an online analyser


Cloud Connectivity

Secure real-time data streaming to big data and analytic software in the cloud

constraint handling

Constraint Handling

Built in constraint handling ensures that the process remains within a safe operating window

Display Components


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