Zero-Norm Distance to Controllability of Linear Systems: Complexity, Bounds, and Algorithms

TL;DR

This paper introduces the zero-norm distance to controllability (ZNDC), a measure of how many parameters need to be changed to make an uncontrollable linear system controllable, and provides complexity analysis, bounds, and heuristic algorithms.

Contribution

It defines ZNDC as a new controllability measure, proves its NP-hardness, and develops two heuristic algorithms for its approximation.

Findings

ZNDC is NP-hard to compute.

Heuristic algorithms perform well on uncontrollable network examples.

Bounds provide insight into the minimal perturbations needed.

Abstract

Determining the distance between a controllable system to the set of uncontrollable systems, namely, the controllability radius problem, has been extensively studied in the past. However, the opposite direction, that is, determining the `distance' between an uncontrollable system to the set of controllable systems, has seldom been considered. In this paper, we address this problem by defining the notion of zero-norm distance to controllability (ZNDC) to be the smallest number of entries (parameters) in the system matrices that need to be perturbed to make the original system controllable. We show genericity exists in this problem, so that other matrix norms (such as the $2$-norm or the Frobenius norm) adopted in this notion are nonsense. For ZNDC, we show it is NP-hard to compute, even when only the state matrix can be perturbed. We then provide some nontrivial lower and upper bounds. For its computation, we provide two heuristic algorithms. The first one is by transforming the ZNDC into a problem of structural controllability of linearly parameterized systems, and then greedily selecting the candidate links according to a suitable objective function. The second one is based on the weighted $l_1$-norm relaxation and the convex-concave procedure, which is tailored for ZNDC when additional structural constraints are involved in the perturbed parameters. Finally, we examine the performance of our proposed algorithms in several typical uncontrollable networks in multi-agent systems.