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

Myung Cho, Aranya Chakrabortty

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


This paper considers an Linear Quadratic Regulator (LQR) design problem for multi-agent distributed control systems where designing an optimal feedback controller by considering communications among agents is desired for the reduction of communication burden in a network. To aim this, we deal with an LQR minimization problem with a regularization for sparse feedback matrix, where the sparsity in the feedback matrix is related to the reduction of the communication links in the multi-agent distributed control systems. We propose a simple but efficient iterative algorithm, so-called Iterative Shrinkage-Thresholding Algorithm (ISTA) for sparse LQR optimal control design. The proposed method can provide a trade-off solution between LQR cost and sparsity level on feedback matrix. Through various numerical experiments, we demonstrate that our proposed method can outperform the previous work using the Alternating Direction Method of Multiplier (ADMM) in terms of computational speed. Additionally, based on our proposed method, we introduce its deep neural network model, which can further improve the performance of the proposed algorithm in convergence speed.

Original languageEnglish (US)
Title of host publication2022 European Control Conference, ECC 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9783907144077
StatePublished - 2022
Event2022 European Control Conference, ECC 2022 - London, United Kingdom
Duration: Jul 12 2022Jul 15 2022

Publication series

Name2022 European Control Conference, ECC 2022


Conference2022 European Control Conference, ECC 2022
Country/TerritoryUnited Kingdom

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Information Systems and Management
  • Control and Systems Engineering
  • Control and Optimization
  • Modeling and Simulation


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