Développement pratique de modèles pour systèmes de commande
Le bon modèle est le plus simple qui fonctionne — pas le plus détaillé que l'on peut construire.
Réserver un appelQuand les modèles deviennent un obstacle
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.
Niveau 1 — Géométrie & physique de base (linéaire)
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.
Niveau 2 — Extensions non linéaires simples (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.
Niveau 3 — Modèles haute-fidélité (rarement justifiés)
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.
Pourquoi les modèles simples l'emportent
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.
Réserver un appel
Si vous avez hérité d'un modèle qui ne fonctionne pas, ou que vous devez en construire un de zéro — un appel de 30 minutes suffit généralement pour cadrer le travail.
Réserver un appel →