Actuator Placement under Structural Controllability using Forward and Reverse Greedy Algorithms

TL;DR

This paper addresses the challenge of actuator placement in complex networks to minimize energy for state transfer, proposing greedy algorithms with performance guarantees under structural controllability constraints.

Contribution

It introduces forward and reverse greedy algorithms for actuator placement, characterizes their feasible sets as matroids, and provides performance guarantees for these algorithms.

Findings

Algorithms perform well on large network case studies.

Performance guarantees are established based on matroid properties.

Feasibility checks are efficiently implemented using maximum flow methods.

Abstract

Actuator placement is an active field of research which has received significant attention for its applications in complex dynamical networks. In this paper, we study the problem of finding a set of actuator placements minimizing the metric that measures the average energy consumed for state transfer by the controller, while satisfying a structural controllability requirement and a cardinality constraint on the number of actuators allowed. As no computationally efficient methods are known to solve such combinatorial set function optimization problems, two greedy algorithms, forward and reverse, are proposed to obtain approximate solutions. We first show that the constraint sets these algorithms explore can be characterized by matroids. We then obtain performance guarantees for the forward and reverse greedy algorithms applied to the general class of matroid optimization problems by exploiting properties of the objective function such as the submodularity ratio and the curvature. Finally, we propose feasibility check methods for both algorithms based on maximum flow problems on certain auxiliary graphs originating from the network graph. Our results are verified with case studies over large networks.