What Has Driven Recent Advances in Control Systems
Read the full article to learn what is driving this progress and why it matters beyond motorsport.
Published
14 NOV 2025
Est. reading time
2 min
Recent progress in control systems has been driven by several key developments. The first is the increase in computational capability, which allows more complex algorithms and models to be run in real time. This has opened the door for techniques such as model predictive control and real-time optimisation to be used in practical applications.
The integration of machine learning and reinforcement learning has also expanded what control systems can achieve. These approaches can uncover patterns and relationships in data that might not be obvious through traditional methods, allowing systems to adapt dynamically to changing conditions.
Hybrid approaches are becoming more common, combining physics-based models with data-driven adjustments. This blend allows engineers to retain the reliability of first-principles modelling while benefiting from the adaptability of machine learning. Advances in automatic code generation have made it easier to take these complex models and deploy them directly to embedded systems.
These tools make it faster to move from concept to implementation while also improving the reliability of the final product. Model updates can be pushed quickly, and controllers can be tuned more precisely to match observed system behaviour.
Data acquisition, post-processing and analytics tools have also improved, providing richer insights and making it easier to solve complex performance and reliability challenges. Engineers are now able to access, interpret and act on large datasets in ways that were not previously feasible within typical project timelines.
While some of these concepts have existed for decades, it is only in the last ten years that computing power and development tools have matured enough to make them practical in real-world engineering.
This is why advanced control system methods are now being applied not only in motorsport, but across aerospace, energy, manufacturing and other high-performance sectors.
They allow teams to build more responsive systems, tune performance under realistic conditions, and deliver results that align more closely with operational goals.
If you have a specific question or business enquiry, please contact us here.