Nonadaptive Learning in Robust Nonlinear Output Regulation
This paper considers robust nonadaptive regulation for general nonlinear systems in an output-feedback setting with arbitrarily high relative degree. We develop a nonadaptive design that combines an input-driven filter and a generic internal model with a recursive backstepping law, thereby recasting the regulation problem as the robust input-to-state stabilization of an augmented error system. Unlike adaptive schemes, the proposed method does not rely on linearly parameterized regressors and does not require the construction of Lyapunov functions having merely nonpositive derivatives. Under standard assumptions on the exosystem, including purely imaginary and simple eigenvalues, together ...
| Authors | Shimin Wang, Martin Guay, Richard D. Braatz |
|---|---|
| Primary category | eess.SY |
| Published | 18 August 2026 |
| Last updated at source | 18 August 2026 |
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