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Data-Driven System Interconnections and a Novel Data-Enabled Internal Model Control
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Title
Data-Driven System Interconnections and a Novel Data-Enabled Internal Model Control
Issued Date
2024-07-12
Citation
Pedari, Yasaman. (2024-07-12). Data-Driven System Interconnections and a Novel Data-Enabled Internal Model Control. 2024 American Control Conference, ACC 2024, 4326–4332. doi: 10.23919/ACC60939.2024.10644828
Type
Conference Paper
ISBN
9798350382655
ISSN
2378-5861
Abstract
Over the past two decades, there has been a growing interest in control systems research to transition from model-based methods to data-driven approaches. In this study, we aim to bridge a divide between conventional model-based control and emerging data-driven paradigms grounded in Willems' 'fundamental lemma'. Specifically, we study how input/output data from two separate systems can be manip-ulated to represent the behavior of interconnected systems, either connected in series or through feedback. Using these results, this paper introduces the Internal Behavior Control (IBC), a new control strategy based on the well-known Internal Model Control (IMC) but viewed under the lens of Behavioral System Theory. Similar to IMC, the IBC is easy to tune and results in perfect tracking and disturbance rejection but, unlike IMC, does not require a parametric model of the dynamics. We present two approaches for IBC implementation: a component-by-component one and a unified one. We compare the two approaches in terms of filter design, computations, and memory requirements. © 2024 AACC.
URI
http://hdl.handle.net/20.500.11750/57855
DOI
10.23919/ACC60939.2024.10644828
Publisher
Institute of Electrical and Electronics Engineers Inc.
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은용순
Eun, Yongsoon은용순

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