Parameter Estimation of Nonlinearly Parameterized Regressions: Application to System Identification and Adaptive Control

Romeo Ortega (1, 2) , Vladislav Gromov (2, 3) , Emmanuel Nuño (4) , Anton Pyrkin (2) , Jose Guadalupe Romero (5)

1 L2S - Laboratoire des signaux et systèmes
2 ITMO University [Russia]
3 Innopolis University
4 Universidad de Guadalajara
5 ITAM - Instituto Tecnológico Autónomo de México

We propose a solution to the problem of parameter estimation of nonlinearly parameterized regressions—continuous or discrete time—and apply it for system identification and adaptive control. We restrict our attention to parameterizations that can be factorized as the product of two functions, a measurable one and a nonlinear function of the parameters to be estimated. Another feature of the proposed estimator is that parameter convergence is ensured without a persistency of excitation assumption. It is assumed that, after a coordinate change, some of the elements of the transformed function satisfy a monotonicity condition. The proposed estimators are applied to design identifiers and adaptive controllers for nonlinearly parameterized systems, which are traditionally tackled using overparameterization and assuming persistency of excitation.

Mots clés: Adaptive control; systems identification; nonlinear control

Domaines: Sciences de l'ingénieur [physics]

Romeo Ortega, Vladislav Gromov, Emmanuel Nuño, Anton Pyrkin, Jose Guadalupe Romero. Parameter Estimation of Nonlinearly Parameterized Regressions: Application to System Identification and Adaptive Control. IFAC 2020 - 21st IFAC World Congress, Jul 2020, Berlin, Germany. pp.1206-1212, ⟨10.1016/j.ifacol.2020.12.1439⟩. ⟨hal-03388003⟩