Sparse Sensing, Communication, and Actuation via Self-Triggered Control Algorithms

TL;DR

This paper introduces a self-triggered control algorithm that optimizes sensor sampling, communication, and actuation in networked control systems to enhance efficiency and reduce resource consumption.

Contribution

It formulates an l0-penalized optimal control problem and demonstrates that its l1-relaxation yields a stabilizing, resource-efficient control law with performance guarantees.

Findings

Feasible l1-relaxation for l0-penalized control problem

Guaranteed stability and performance bounds

Sparse sampling and communication schedule

Abstract

We propose a self-triggered control algorithm to reduce onboard processor usage, communication bandwidth, and energy consumption across a linear time-invariant networked control system. We formulate an optimal control problem by penalizing the l0-measures of the feedback gain and the vector of control inputs and maximizing the dwell time between the consecutive triggering times. It is shown that the corresponding l1-relaxation of the optimal control problem is feasible and results in a stabilizing feedback control law with guaranteed performance bounds, while providing a sparse schedule for collecting samples from sensors, communication with other subsystems, and activating the input actuators.