What does this research mean for the field?

An adaptive observer utilizing a ̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃̃\u

What question did this study set out to answer?

To design an adaptive observer that reconstructs unmeasured state variables and estimates unknown constant parameters in nonlinear cascade systems subject to model uncertainties.

September 5, 2026AutomationOpen Access

Adaptive Observer Design for Partially Measured Nonlinear Cascade Systems with Unknown Parameters

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Authors

FPFrancesco PierriUniversity of Basilicata GCGraziano CarrieroUniversity of Basilicata MSMonica SileoUniversity of Basilicata

Discussion

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Implication

Simulation study demonstrates robust state and parameter estimation in nonlinear cascade systems, highlighting resilient observer performance under measurement noise and uncertainties.

Key Points

To design an adaptive observer that reconstructs unmeasured state variables and estimates unknown constant parameters in nonlinear cascade systems subject to model uncertainties.
Formulated a Lyapunov-based framework to derive observer gain conditions guaranteeing asymptotic convergence of state estimation errors.
Incorporated a sigma-modified adaptation law to suppress parameter drift caused by bounded model uncertainties.
Evaluated observer convergence and robustness via numerical simulations of a reduced-order marine vessel model with environmental disturbances and measurement noise.
Theoretical stability proofs established asymptotic convergence of state errors in ideal settings and uniform ultimate boundedness under bounded uncertainties.
Numerical tests on a marine vessel model confirmed rapid convergence of state and parameter estimates while eliminating parameter drift.
Sensitivity analyses demonstrated that the observer maintains stable, bounded estimation errors even in the presence of noisy output measurements.

Cite This Study

Pierri et al. (2026) studied this question.

synapsesocial.com/papers/6a9bd48c6b95aff0620ec4c5 https://doi.org/10.3390/automation7050139

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September 5, 2026AutomationOpen Access

Adaptive Observer Design for Partially Measured Nonlinear Cascade Systems with Unknown Parameters

View Full Paper

Ask AI

Bookmark

Authors

FPFrancesco PierriUniversity of Basilicata GCGraziano CarrieroUniversity of Basilicata MSMonica SileoUniversity of Basilicata

Discussion

Member takes

Implication

Simulation study demonstrates robust state and parameter estimation in nonlinear cascade systems, highlighting resilient observer performance under measurement noise and uncertainties.

Key Points

To design an adaptive observer that reconstructs unmeasured state variables and estimates unknown constant parameters in nonlinear cascade systems subject to model uncertainties.
Formulated a Lyapunov-based framework to derive observer gain conditions guaranteeing asymptotic convergence of state estimation errors.
Incorporated a sigma-modified adaptation law to suppress parameter drift caused by bounded model uncertainties.
Evaluated observer convergence and robustness via numerical simulations of a reduced-order marine vessel model with environmental disturbances and measurement noise.
Theoretical stability proofs established asymptotic convergence of state errors in ideal settings and uniform ultimate boundedness under bounded uncertainties.
Numerical tests on a marine vessel model confirmed rapid convergence of state and parameter estimates while eliminating parameter drift.
Sensitivity analyses demonstrated that the observer maintains stable, bounded estimation errors even in the presence of noisy output measurements.

Cite This Study

Pierri et al. (2026) studied this question.

synapsesocial.com/papers/6a9bd48c6b95aff0620ec4c5 https://doi.org/10.3390/automation7050139

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