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arXiv AI By Shimin Wang, Martin Guay, Richard D. Braatz

Nonadaptive Learning in Robust Nonlinear Output Regulation

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This paper presents a nonadaptive approach to robust nonlinear output regulation for general nonlinear systems with high relative degree in an output‑feedback setting. The design combines an input‑driven filter, a generic internal model, and a recursive backstepping law, transforming the regulation problem into robust input‑to‑state stabilization of an augmented error system. Under standard exosystem assumptions and a minimum‑phase input‑to‑state stability condition, the authors prove global asymptotic regulation and provide explicit inequalities for selecting design gains, demonstrating the method on a benchmark Duffing system.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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