Some Remarks on Practical Stabilization via CLF-Based Control Under Measurement Noise

Practical stabilization of input-affine systems in the presence of measurement errors and input constraints is considered in this brief note. Assuming that a Lyapunov function and a stabilizing control exist for an input-affine system, the required measurement accuracy at each point of the state space is computed. This is done via the Lyapunov function-based decay condition, which describes along with the input constraints a set of admissible controls. Afterwards, the measurement time points are computed based on the system dynamics. It is shown that between these self-triggered measurement time points, the system evolves and converges into the so-called target ball, i.e. a vicinity of the origin, where it remains. Furthermore, it is shown that the approach ensures the existence of a control law, which is admissible for all possible states and it introduces a connection between measurement time points, measurement accuracy, target ball, and decay. The results of the approach are shown in two examples.

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Publication Details

Journal
IEEE Access
Published
2024-12-23
DOI
https://doi.org/10.1109/access.2024.3521048
Primary Topic
Iterative Learning Control Systems
Type
article
Field-Weighted Citation Impact
0.00

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Controls
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article

Some Remarks on Practical Stabilization via CLF-Based Control Under Measurement Noise

Stefan Streif, Pavel Osinenko, Patrick Schmidt
IEEE Access
Iterative Learning Control Systems
article

Some Remarks on Practical Stabilization via CLF-Based Control Under Measurement Noise

Stefan Streif, Pavel Osinenko, Patrick Schmidt
article en

Abstract

Practical stabilization of input-affine systems in the presence of measurement errors and input constraints is considered in this brief note. Assuming that a Lyapunov function and a stabilizing control exist for an input-affine system, the required measurement accuracy at each point of the state space is computed. This is done via the Lyapunov function-based decay condition, which describes along with the input constraints a set of admissible controls. Afterwards, the measurement time points are computed based on the system dynamics. It is shown that between these self-triggered measurement time points, the system evolves and converges into the so-called target ball, i.e. a vicinity of the origin, where it remains. Furthermore, it is shown that the approach ensures the existence of a control law, which is admissible for all possible states and it introduces a connection between measurement time points, measurement accuracy, target ball, and decay. The results of the approach are shown in two examples.

IEEE AccessVol. 13
Skolkovo Institute of Science and Technology (RU), Chemnitz University of Technology (DE)
Bundesministerium für Bildung und Forschung
Affordable and clean energy
Openalex Percentile: Top 72%
Iterative Learning Control Systems
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