Iterative Thresholding and Projection Algorithms and Model-Based Deep Neural Networks for Sparse LQR Control Design

TL;DR

This paper introduces iterative algorithms and model-based neural networks for designing sparse LQR controllers in large-scale distributed systems, reducing communication links while maintaining control performance.

Contribution

It proposes simple iterative algorithms and neural network models that improve computational efficiency and sparsity in distributed LQR control design.

Findings

Algorithms outperform ADMM and GraSP methods.

Neural networks accelerate convergence speed.

Sparse feedback matrices reduce communication links.

Abstract

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 simple but efficient iterative algorithms -- Iterative Shrinkage Thresholding Algorithm (ISTA) and Iterative Sparse Projection Algorithm (ISPA). They 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 algorithms, we design deep neural network models based on the proposed iterative algorithms. Numerical experiments demonstrate that our algorithms can outperform the previous methods using the Alternating Direction Method of Multiplier (ADMM) [2] and the Gradient Support Pursuit (GraSP) [3], and their deep neural network models can improve the performance of the proposed algorithms in convergence speed.