Near-Optimal Distributed Linear-Quadratic Regulator for Networked Systems
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2022
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| _version_ | 1866914543935946752 |
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| author | Shin, Sungho Lin, Yiheng Qu, Guannan Wierman, Adam Anitescu, Mihai |
| author_facet | Shin, Sungho Lin, Yiheng Qu, Guannan Wierman, Adam Anitescu, Mihai |
| contents | This paper studies the trade-off between the degree of decentralization and the performance of a distributed controller in a linear-quadratic control setting. We study a system of interconnected agents over a graph and a distributed controller, called $κ$-distributed control, which lets the agents make control decisions based on the state information within distance $κ$ on the underlying graph. This controller can tune its degree of decentralization using the parameter $κ$ and thus allows a characterization of the relationship between decentralization and performance. We show that under mild assumptions, including stabilizability, detectability, and a subexponentially growing graph condition, the performance difference between $κ$-distributed control and centralized optimal control becomes exponentially small in $κ$. This result reveals that distributed control can achieve near-optimal performance with a moderate degree of decentralization, and thus it is an effective controller architecture for large-scale networked systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2204_05551 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Near-Optimal Distributed Linear-Quadratic Regulator for Networked Systems Shin, Sungho Lin, Yiheng Qu, Guannan Wierman, Adam Anitescu, Mihai Optimization and Control Machine Learning Systems and Control Dynamical Systems This paper studies the trade-off between the degree of decentralization and the performance of a distributed controller in a linear-quadratic control setting. We study a system of interconnected agents over a graph and a distributed controller, called $κ$-distributed control, which lets the agents make control decisions based on the state information within distance $κ$ on the underlying graph. This controller can tune its degree of decentralization using the parameter $κ$ and thus allows a characterization of the relationship between decentralization and performance. We show that under mild assumptions, including stabilizability, detectability, and a subexponentially growing graph condition, the performance difference between $κ$-distributed control and centralized optimal control becomes exponentially small in $κ$. This result reveals that distributed control can achieve near-optimal performance with a moderate degree of decentralization, and thus it is an effective controller architecture for large-scale networked systems. |
| title | Near-Optimal Distributed Linear-Quadratic Regulator for Networked Systems |
| topic | Optimization and Control Machine Learning Systems and Control Dynamical Systems |
| url | https://arxiv.org/abs/2204.05551 |