Adaptive immersion-and-invariance control with normalizing regressor filter
File(s)
Accepted version
Author(s)
Chen, Kaiwen
Astolfi, Alessandro
Type
Conference Paper
Abstract
This paper proposes a new scheme for the so-called adaptive immersion-and-invariance (I&I) control that requires neither solving partial differential equations nor adding dynamic scaling factors to obtain the static I&I parameter estimation term. By exploiting a specially designed time-varying candidate Lyapunov function, we show that it is sufficient to pass the regressor through a strictly passive filter with a normalization-like output nonlinearity to generate a proxy regressor that forms the static I&I estimate. Closed-loop boundedness and asymptotic stabilization can be guaranteed. Simulation results illustrate the theory.
Date Issued
2026-01-12
Date Acceptance
2025-12-01
Citation
2025 IEEE 64th Conference on Decision and Control (CDC), 2026, pp.717-722
Publisher
IEEE
Start Page
717
End Page
722
Journal / Book Title
2025 IEEE 64th Conference on Decision and Control (CDC)
Copyright Statement
Copyright © 2025, IEEE. This is the author’s accepted manuscript made available under a CC-BY licence in accordance with Imperial’s Research Publications Open Access policy (www.imperial.ac.uk/oa-policy)
License URL
Source
2025 IEEE 64th Conference on Decision and Control (CDC)
Publication Status
Published
Start Date
2025-12-09
Finish Date
2025-12-12
Coverage Spatial
Rio de Janeiro, Brazil