Minimum Cost Input/Output Design for Large Scale Linear Structural Systems

TL;DR

This paper develops polynomial-time algorithms for optimally placing inputs and outputs in large-scale linear systems to minimize costs while ensuring controllability and observability, including a constrained variant with minimal actuated/measured states.

Contribution

It introduces efficient algorithms for optimal input/output placement in large-scale systems considering cost variations and constraints, advancing structural control design methods.

Findings

Algorithms are polynomial-time solvable.

Optimal placement minimizes costs while ensuring controllability/observability.

The approach is demonstrated with a practical example.

Abstract

In this paper, we provide optimal solutions to two different (but related) input/output design problems involving large-scale linear dynamical systems, where the cost associated to each directly actuated/measured state variable can take different values, but is independent of the labeled input/output variable. Under these conditions, we first aim to determine and characterize the input/output placement that incurs in the minimum cost while ensuring that the resulting placement achieves structural controllability/observability. Further, we address a constrained variant of the above problem, in which we seek to determine the minimum cost placement configuration, among all possible input/output placement configurations that ensures structural controllability/observability, with the lowest number of directly actuated/measured state variables. We show that both problems can be solved efficiently, i.e., using algorithms with polynomial time complexity in the number of the state variables. Finally, we illustrate the obtained results with an example.