Control
Funnel Control
Funnel control is a model-free adaptive control methodology that guarantees a system's tracking error remains inside a prescribed, typically shrinking, time-varying boundary called the funnel, by increasing feedback gain as the error approaches the funnel wall. Introduced by Ilchmann, Ryan, and Sangwin (2002), it provides transient and steady-state performance guarantees without a plant model. Relatedly, funnels in motion planning denote verified regions of attraction composed sequentially, as in LQR-trees.
Why it matters for physical AI
Prescribed-performance guarantees without accurate models suit robots whose dynamics are uncertain or learned, and funnel composition ideas inform how verified safety certificates can wrap around otherwise unverified learned controllers.
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