• MPC goes to automotive production

    ODYS and General Motors bring embedded MPC to high-volume production!

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  • Automotive Control

    Design and deployment of real-time embedded MPC systems for automotive production.

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  • Aerospace

    Real-time optimization tools for designing guidance, navigation and control modules for space applications.

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  • Energy

    Automated and risk-aware decision strategies for optimal smart grid management and market operations.

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  • Advanced Process Control

    MPC and machine learning solutions for optimizing productivity, quality, energy and costs in industrial processes.

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Embedded MPC for Next-Gen Controls

We are specialized in developing Model Predictive Control (MPC) systems for next-gen controls in industrial production. Based on more than 25 years of scientific research, our expertise covers advanced multivariable control design, efficient real-time optimization algorithms, and tools for their deployment in production. We focus on embedded systems and applications in the automotive, aerospace, energy, and process control domains.

Read more about our QP solver and embedded MPC toolchain. Have a look at our consulting and engineering services and some of the projects we have carried out.

Advanced Design

We support R&D and production engineers over the entire MPC design process: problem formulation, system identification, customization of advanced MPC features, tuning in co-simulation.

Efficient optimization

Thanks to our state-of-the-art optimization library, we pick the best solver for the problem at hand, enabling fast and robust execution of embedded MPC systems in real-time.

Easy Deployment

We provide automatic code generation tools that enable seamless integration and fast deployment of embedded MPC in different control platforms.

Application Domains

We help automotive engineers developing advanced control systems based on embedded MPC and machine learning.

We have experience in engine control, powertrain coordination, autonomous driving, vehicle dynamics, valet parking, exhaust aftertreatment, on-board diagnostics (OBD), and more.

Embedded MPC for next-gen controls
We design advanced real-time software tolls for guidance, navigation, and control in the space industry. Applications include satellite attitude control, space rendezvous, and autonomous navigation of drones.
Embedded MPC for next-gen controls
We design real-time management systems that can control power flows in smart grids and place bids on the energy markets, to maximize profits in a risk-sensitive way under uncertainty due to load, energy prices, and renewables.
Embedded MPC for next-gen controls
Process engineers in the pulp and paper, steel, petrochemical, and pharmaceutical industries take advantage of our know-how in embedded MPC to develop customized process control solutions that optimize productivity and save energy under the best use of available resources.
Embedded MPC for next-gen controls

Industrial Projects

See more of our industrial projects »

Some of our Clients

Embedded MPC for next-gen controls - General Motors     Embedded MPC for next-gen controls - Ford     Embedded MPC for next-gen controls - Denso

    Embedded MPC for next-gen controls - Astrium               Embedded MPC for next-gen controls - Milltech     Embedded MPC for next-gen controls - Trillary

Funded Projects

Embedded MPC for next-gen controls - RETROFEED
RETROFEED: Implementation of a smart RETROfitting framework in the process industry
towards its operation with variable, biobased and circular FEEDstock

EU H2020
2019 – 2023

Embedded MPC for next-gen controls - GUIBEAR
GUIBEAR: Prototyping of Bearings-Only Guidance of Rendezvous in NRO Orbits
Funded by the European Space Agency
2019 – 2020

Embedded MPC for next-gen controls - MELiSSA
PaCMan: Plant characterization unit for closed life support system – engineering, manufacturing & testing
Part of the MELiSSA Project – Funded by the European Space Agency
2018 – 2019

Embedded MPC for next-gen controls - oCPSoCPS: Platform-aware Model-driven Optimization of Cyber-Physical Systems
Marie Curie Innovative Training Network
2016 – 2018