How Model-Based Control Systems Solve Complex Problems
Read the full article to see how this method is applied in motorsport, aerospace, energy and advanced manufacturing.
Published
17 NOV 2025
Est. reading time
2 min
Model-based control systems use mathematical or physics-based models to predict and optimise system behaviour. These models can represent a single physical domain, such as electrical or mechanical, or span multiple domains when the system integrates disciplines like hydraulics, thermal dynamics and electronics. This approach enables accurate simulation, controller tuning and performance validation before deployment.
By understanding and replicating the dynamics of the system, engineers can develop control strategies that predict behaviour, anticipate changes and optimise performance. This is particularly valuable when dealing with non-linear systems, where behaviour does not follow a simple, predictable pattern.
Non-linearity often means that system responses change depending on the conditions. Model-based control lets engineers capture these subtleties, resulting in better control accuracy and more robust performance across varying loads and environments.
Advanced techniques such as multi-sensor control loops, sensor fusion and state estimators allow the system to work with both measurable and unmeasurable parameters. For example, the state of charge of a battery cannot be measured directly, but it is a critical factor in determining how the system operates. State estimators, such as Kalman filters, can infer this information from other measurements, enabling precise and reliable control.
These tools are essential when working with systems that require indirect observation of key variables. They improve responsiveness, reduce uncertainty and support control logic that remains stable under real-world constraints.
These capabilities are not limited tomotorsport. In aerospace, they help manage propulsion and flight control in complex environments. In energy systems, they balance supply and demand while protecting critical infrastructure. In manufacturing, they optimise machinery to deliver maximum throughput without sacrificing quality.
By predicting rather than simply reacting, model-based control systems deliver stability, efficiency and performance even under challenging and unpredictable conditions.
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