On the Optimal Control of Network LQR with Spatially-Exponential Decaying Structure
Not on the map. Why?
Abstract (arXiv)
This paper studies network LQR problems with system matrices being spatially-exponential decaying (SED) between nodes in the network. The major objective is to study whether the optimal controller also enjoys a SED structure, which is an appealing property for ensuring the optimality of decentralized control over the network. We start with studying the open-loop asymptotically stable system and show that the optimal LQR state feedback gain K is `quasi'-SED in this setting, i.e. |[K]ij|~ Oleft(e^-fraccpolyln(N)dist(i,j)right). The decaying rate c depends on the decaying rate and norms of system matrices and the open-loop exponential stability constants. Then the result is further generalized to unstable systems under a SED stabilizability assumption. Building upon the `quasi'-SED result on K, we give an upper-bound on the performance of kappa-truncated local controllers, suggesting that distributed controllers can achieve near-optimal performance for SED systems. We develop these results via studying the structure of another type of controller, disturbance response control, which has been studied and used in recent online control literature; thus as a side result, we also prove the `quasi'-SED property of the optimal disturbance response control, which serves as a contribution on its own merit.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Sep 2022.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2022: 1; 2023: 0; 2024: 1; 2025: 1.
3 papers.
- 2022Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learningcited by 18
- 2025Exploiting inter-agent coupling information for efficient reinforcement learning of cooperative LQRcited by 1
- 2024Distributed Policy Gradient for Linear Quadratic Networked Control with Limited Communication Rangecited by 0
- 2025Exploiting inter-agent coupling information for efficient reinforcement learning of cooperative LQR29 Apr 2025
- 2024Distributed Policy Gradient for Linear Quadratic Networked Control with Limited Communication Range5 Mar 2024
- 2022Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning30 Nov 2022