Compressive Sampling for Networked Feedback Control

TL;DR

This paper explores using compressive sampling with L1-L2 optimization to efficiently transmit control vectors in networked feedback systems, improving performance under bandwidth constraints.

Contribution

It introduces a sparsity-promoting control scheme utilizing FISTA for efficient compression, outperforming traditional energy-limited control methods.

Findings

Better control performance than conventional L2-optimal control

Efficient compression via L1-L2 optimization and FISTA

Effective in rate-limited networked feedback scenarios

Abstract

We investigate the use of compressive sampling for networked feedback control systems. The method proposed serves to compress the control vectors which are transmitted through rate-limited channels without much deterioration of control performance. The control vectors are obtained by an L1-L2 optimization, which can be solved very efficiently by FISTA (Fast Iterative Shrinkage-Thresholding Algorithm). Simulation results show that the proposed sparsity-promoting control scheme gives a better control performance than a conventional energy-limiting L2-optimal control.