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Multirate sampling based data-driven control
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DOI：10.1080/00207721.2025.2504049.png)
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Abstract
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This paper explores multirate sampling as a data collection method for parameter estimation and control design of unknown LTI systems. It is shown that multirate sampled data, used to represent the input-output mapping of the unknown system, presents an effective way to handle the external unknown disturbance in the system. Differential rates for input and output samplings allow for a more intricate understanding of system output dynamics while maintaining a constant input. This helps in designing a compensation term to mitigate the effect of disturbance in the output. It is also observed that employing multirate sampling for control design and parameter estimation reduces the number of parameters affecting the control. The unknown system is represented as a Deterministic Auto-Regressive Moving Average (DARMA) model. The proposed strategy is a combination of offline estimation and online control design, and its efficacy has been verified using a simulation study.
Keywords:
Deterministic
discrete system
disturbance
DARMA model
multirate sampling
robust control
ARMAX
ARMA
ARX
AR models
models
control
discrete time systems
systems
linear systems
optimal control
Journal
I
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4.6
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1.1K
Citations:
7.3K
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Cited Papers
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Proceedings of the First International Conference on Informatics in Control, Automation and Robotics
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Proceedings of the First International Conference on Informatics in Control, Automation and RoboticsIF0
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