New Paper on Robust Nonadaptive Regulation for Nonlinear Systems

A recent preprint on arXiv (2608.17262) introduces a nonadaptive strategy for achieving robust output regulation in general nonlinear systems, specifically within output-feedback contexts that allow for any relative degree. This innovative method integrates an input-driven filter with a versatile internal model and employs recursive backstepping, transforming the challenge into robust input-to-state stabilization of an enhanced error system. In contrast to adaptive methods, it does not depend on linearly parameterized regressors or Lyapunov functions with solely nonpositive derivatives. The authors demonstrate global asymptotic regulation under typical exosystem conditions—purely imaginary, simple eigenvalues—and a minimum-phase input-to-state stability requirement, offering explicit verifiable inequalities for selecting gains.