Adaptive learning control for nonlinear systems: A single learning estimation scheme is enough

In this brief, continuous-time nonlinear systems with extended matching uncertainties are considered. The problem of designing a state-feedback adaptive learning control of reduced complexity — just including a single adaptive learning estimation scheme in the upper subsystem and a high-gain proport...

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Bibliographic Details
Published in:Automatica (Oxford) 2023-03, Vol.149, p.110833, Article 110833
Main Authors: Verrelli, Cristiano Maria, Tomei, Patrizio
Format: Article
Language:English
Subjects:
Extended matching
Learning control
Output tracking
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Summary:In this brief, continuous-time nonlinear systems with extended matching uncertainties are considered. The problem of designing a state-feedback adaptive learning control of reduced complexity — just including a single adaptive learning estimation scheme in the upper subsystem and a high-gain proportional action in the input channel — is addressed. By properly setting the control parameters, exponential output tracking of (sufficiently smooth) periodic reference signals with a known period is achieved. Fourier series expansions are used and estimates of the resulting Fourier coefficients are continuously adapted based on the persistency of excitation conditions that naturally hold due to the orthogonal nature of the sinusoidal basis functions.
ISSN:0005-1098
1873-2836
DOI:10.1016/j.automatica.2022.110833