Practical Model Development for Control Systems
The right model is the simplest one that works — not the most detailed one you can build.
Book a Discovery CallWhen Models Get in the Way
A client once commissioned a dynamic model derived from video-game simulation code. It was elaborate and computationally expensive — but when the time came to connect it to a control loop, the interface didn't match any standard toolchain, the parameters had no physical meaning, and the controller designed against it performed worse than a hand-tuned PID on the real hardware.
Over-engineered models are a recurring problem in control engineering: they take months to build, break when the physical system changes, are hard to validate against measurements, and often cannot be connected to real control software without a bespoke translation layer.
The solution is not a better complex model. It is starting from the simplest model that can capture the behaviour that matters for control.
Tier 1 — Geometry & Basic Physics (Linear)
Derive mass, inertia tensor, spring and damper rates, kinematic constraints, and basic aerodynamic coefficients directly from geometry, drawings, or CAD. Linearise around the operating point to obtain a state-space model.
- Suitable for Bode analysis, loop shaping, LQR, and linear MPC synthesis
- Parameters carry physical meaning — straightforward to validate against step-response data
- Usually sufficient for feedback control design and gain tuning
- Compatible with any standard control toolchain out of the box
Start here. This is enough most of the time.
Tier 2 — Simple Nonlinear Extensions (Simulation)
Add only the nonlinearities the application actually requires: actuator saturation, dead-band, significant kinematic coupling, or dominant nonlinear stiffness terms.
- Used for closed-loop simulation before hardware deployment
- Parameters still have physical meaning — no black-box fitting
- Light enough for real-time use on standard hardware
- Gives realistic margin estimates that the linear model cannot provide
Add this when the linear model produces controllers that fail in simulation.
Tier 3 — High-Fidelity Models (Rarely Justified)
Justified only when tiers 1 and 2 genuinely cannot capture safety-critical behaviour. Examples: flexible structures with significant modal coupling, combustion and reaction kinetics, two-phase flow, or aeroelastic effects.
- Model complexity becomes part of the research problem, not a byproduct
- Requires dedicated identification experiments and validation campaigns
- Longer build time is accepted because the simpler tiers are demonstrably insufficient
Reach for this only when you have evidence that tier 2 is not enough.
Why Simpler Models Win
Built in Days, Not Months
First-principles derivation is fast. A working linear model for a new system typically takes a few days of engineering effort — not a six-month modelling project.
Interpretable Parameters
Every parameter has a physical meaning: mass, inertia, stiffness, damping ratio. You can validate against measurements and update the model when the hardware changes.
Toolchain Compatible
State-space models drop directly into MATLAB, Python-Control, CasADi, and any MPC solver. No interface translation layer, no bespoke simulation environment required.
Honest About Uncertainty
A simple model you know is approximate is safer than a complex model you assume is accurate. Knowing the model limits is part of the control design.
Book a Discovery Call
If you've inherited a model that doesn't work, or need to build one from scratch — a 30-minute call is usually enough to scope the task.
Book a Discovery Call →