Probabilistic Sufficient Conditions for Prediction-Based Stabilization of Linear Systems With Random Input Delay

This letter focuses on the prediction-based stabilization of a linear system subject to a random input delay. Modeling the delay as a finite-state Markov process, it proves that a constant time-horizon prediction enables robust compensation of the delay, provided the horizon prediction is sufficient...

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Bibliographic Details
Published inIEEE control systems letters Vol. 6; pp. 2270 - 2275
Main Authors Kong, Sijia, Bresch-Pietri, Delphine
Format Journal Article
LanguageEnglish
Published IEEE 2022
Subjects
Automatic
Backstepping
Delay systems
Delays
distributed parameter systems
Engineering Sciences
Linear systems
Markov processes
prediction-based controller
Probabilistic logic
Stability analysis
distributed parameter systems
Delay systems
prediction-based controller
Delay systems prediction-based controller distributed parameter systems
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Summary:This letter focuses on the prediction-based stabilization of a linear system subject to a random input delay. Modeling the delay as a finite-state Markov process, it proves that a constant time-horizon prediction enables robust compensation of the delay, provided the horizon prediction is sufficiently close to the delay values in average. Simulation results emphasize the practical relevance of this condition.
ISSN:2475-1456
2475-1456
DOI:10.1109/LCSYS.2022.3141495