Communication-Efficient Optimal Control Design For Distributed Control Systems In Cooperative Vehicular Networks

Myung, Cho; Abdallah, Abdallah S.

doi:10.1109/vtc2020-spring48590.2020.9129627

Cited by 4 publications

(3 citation statements)

References 17 publications

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“…To reduce the number of communcation links in a network, the sparse LQR optimal control design problem or its variation has been studied in previous research such as [14][15][16][17] and reference therein. In order to have a sparse feedback matrix which represents the reduction of the number of communication links in a network, for the regularization term G(K), element-wise 1 norm or its variation were considered, e.g., G(K) = K 1 , or 1 norm of off-diagonal matrix of K, or column-wise 1 norm.…”

Section: Previous Research On Lqr Control Designmentioning

confidence: 99%

“…More specifically, in [4][5][6][7][8][9][10][11][12][13] and the references therein, LQR control designs with predetermined structure of network topologies were studied. In order to reduce the number of communication links in a distributed multi-agents system, the proposed solutions in [10,[14][15][16][17] took into account sparse LQR control design models which simultaneously minimize LQR cost as well as sparsity level of the network topology by considering the sparsity condition on the topology as a regularization term or as a constraint in optimization problems.…”

Section: Introductionmentioning

confidence: 99%

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Low-rank LQR Optimal Control Design over Wireless Communication Networks

Cho¹,

Abdallah²,

Rasouli³

2023

Preprint

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This paper considers a LQR optimal control design problem for distributed control systems with multi-agents. To control large-scale distributed systems such as smart-grid and multi-agent robotic systems over wireless communication networks, it is desired to design a feedback controller by considering various constraints on communication such as limited power, limited energy, or limited communication bandwidth, etc. In this paper, we focus on the reduction of communication energy in an LQR optimal control design problem on wireless communication networks. By considering the characteristic of wireless communication, i.e., Radio Frequency (RF) signal can spread in all directions in a broadcast way, we formulate a low-rank LQR optimal control model to reduce the communication energy in the distributed feedback control system. To solve the problem, we propose an Alternating Direction Method of Multipliers (ADMM) based algorithm. Through various numerical experiments, we demonstrate that a feedback controller designed using lowrank structure can outperform the previous work on sparse LQR optimal control design, which focuses on reducing the number of communication links in a network, in terms of energy consumption, system stability margin against noise and error in communication.

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Section: Previous Research On Lqr Control Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Low-rank LQR Optimal Control Design over Wireless Communication Networks

Cho¹,

Abdallah²,

Rasouli³

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…A sparsity promoting charging control model of plug‐in electric vehicles has been addressed in [27]. In [28] an optimal LQR control for cooperative vehicular networks has been designed.…”

Section: Introductionmentioning

confidence: 99%

Robust decentralized control of interval DC linked hybrid microgrids network

Nabi

Seyedtabaii

2022

IET Control Theory & Appl

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Here, a robust sparse decentralized controller (RSDC) for uncertain DC-linked hybrid microgrids is designed. The robustness against load/generation uncertainty is guaranteed using Kharitonov's stability criteria and sparsity is promoted by appending an l 1 term to the typical Linear Quadratic Regulator (LQR). To improve the convergence rate, the differentiable term of the composite cost function is second order approximated, the coordinate descent technique is adopted, and the "active set" idea which limits the search to the effective regions is employed. In addition, the level of sparsity is automatically and optimally adjusted not to compromise the system's stability and robustness. The algorithm is applied to a 69-bus (consist of 7 hybrid microgrid) distribution system and the controller sparsity percentage, design convergence rate, and performances are compared with the H 2 and H ∞ designs. The results indicate that the proposed method is superior to the others in all aspects, which are presented by the numerical indices, the time-domain transient responses, and the closed-loop poles map.

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Iterative Shrinkage-Thresholding Algorithm and Model-Based Neural Network for Sparse LQR Control Design

Cho

Chakrabortty

2022

2022 European Control Conference (ECC)

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In this paper, we consider an LQR design problem for distributed control systems. For large-scale distributed systems, finding a solution might be computationally demanding due to communications among agents. To this aim, we deal with LQR minimization problem with a regularization for sparse feedback matrix, which can lead to achieve the reduction of the communication links in the distributed control systems. For this work, we introduce a simple but efficient iterative algorithm. It can give us a trade-off solution between LQR cost and sparsity level on feedback matrix. Moreover, in order to improve the speed of the proposed algorithm, we design deep neural network models based on the proposed iterative algorithm. Numerical experiments demonstrate that our algorithm outperforms the previous method using the Alternating Direction Method of Multiplier (ADMM), and its deep neural network models can improve the performance of the proposed algorithm in convergence speed.

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Communication-Efficient Optimal Control Design For Distributed Control Systems In Cooperative Vehicular Networks

Cited by 4 publications

References 17 publications

Low-rank LQR Optimal Control Design over Wireless Communication Networks

Low-rank LQR Optimal Control Design over Wireless Communication Networks

Robust decentralized control of interval DC linked hybrid microgrids network

Iterative Shrinkage-Thresholding Algorithm and Model-Based Neural Network for Sparse LQR Control Design

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