# A Framework for Structural Input/Output and Control Configuration Selection in Large-Scale Systems

arXiv: https://arxiv.org/abs/1309.5868 · HTML: https://mlsanity.com/paper/1309.5868/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Sep 2013.

## Built on by (most cited first, 1 of 1)

- [Minimize Control Inputs for Strong Structural Controllability Using Reinforcement Learning with Graph Neural Network](https://mlsanity.com/paper/2402.16925/) (2024)

## Abstract (arXiv)

This paper addresses problems on the structural design of control systems taking explicitly into consideration the possible application to large-scale systems. We provide an efficient and unified framework to solve the following major minimization problems: (i) selection of the minimum number of manipulated/measured variables to achieve structural controllability/observability of the system, and (ii) selection of the minimum number of feedback interconnections between measured and manipulated variables such that the closed-loop system has no structurally fixed modes. Contrary to what would be expected, we show that it is possible to obtain a global solution for each of the aforementioned minimization problems using polynomial complexity algorithms in the number of the state variables of the system. In addition, we provide several new graph-theoretic characterizations of structural systems concepts, which, in turn, enable us to characterize all possible solutions to the above problems.