A Data-driven Hierarchical Control Structure for Systems with Uncertainty
Lu Shi, Hanzhe Teng, Xinyue Kan, and Konstantinos Karydis

TL;DR
This paper presents a data-driven hierarchical control framework that enhances system performance under uncertainty by combining model identification with a higher-level controller, suitable for real-time robotic applications.
Contribution
The paper introduces a novel hierarchical control structure that learns system models from limited data and maintains stability, improving robustness in uncertain environments.
Findings
Effective in simulation and real-world aerial robot experiments.
Requires minimal data for online adaptation.
Maintains system stability despite uncertainties.
Abstract
The paper introduces a Data-driven Hierarchical Control (DHC) structure to improve performance of systems operating under the effect of system and/or environment uncertainty. The proposed hierarchical approach consists of two parts: 1) A data-driven model identification component to learn a linear approximation between reference signals given to an existing lower-level controller and uncertain time-varying plant outputs. 2) A higher-level controller component that utilizes the identified approximation and wraps around the existing controller for the system to handle modeling errors and environment uncertainties during system deployment. We derive loose and tight bounds for the identified approximation's sensitivity to noisy data. Further, we show that adding the higher-level controller maintains the original system's stability. A benefit of the proposed approach is that it requires…
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Taxonomy
TopicsControl Systems and Identification · Advanced Control Systems Optimization · Model Reduction and Neural Networks
