Actuator Scheduling for Linear Systems: A Convex Relaxation Approach

TL;DR

This paper introduces a convex relaxation method for actuator scheduling in networked control systems, providing an efficient algorithm with performance bounds and extensions to multiple actuators and costs.

Contribution

It presents a novel convex relaxation approach for actuator scheduling, along with an algorithm that offers suboptimality bounds and extensions to more complex scenarios.

Findings

Outperforms random and greedy methods in simulations

Provides a suboptimality bound for the scheduling algorithm

Extends to multiple actuators and cost considerations

Abstract

In this letter, we investigate the problem of actuator scheduling for networked control systems. Given a stochastic linear system with a number of actuators, we consider the case that one actuator is activated at each time. This problem is combinatorial in nature and NP hard to solve. We propose a convex relaxation to the actuator scheduling problem, and use its solution as a reference to design an algorithm for solving the original scheduling problem. Using dynamic programming arguments, we provide a suboptimality bound of our proposed algorithm. Furthermore, we show that our framework can be extended to incorporate multiple actuators scheduling at each time and actuation costs. A simulation example is provided, which shows that our proposed method outperforms a random selection approach and a greedy selection approach.