Compressive Sampling for Networked Feedback Control

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.

References in corpus (2)

• Compressive Sampling for Remote Control Systems
• Sparse Command Generator for Remote Control

Cited by in corpus (5)

• Sparse Packetized Predictive Control for Networked Control over Erasure Channels
• Discrete and Continuous-time Soft-Thresholding with Dynamic Inputs
• Packetized Predictive Control for Rate-Limited Networks via Sparse Representation
• Min-max piecewise constant optimal control for multi-model linear systems
• Networked Estimation using Sparsifying Basis Prediction