A Framework for Structural Input/Output and Control Configuration Selection in Large-Scale Systems
For control engineers designing large-scale systems, this work offers efficient, globally optimal solutions to fundamental structural design problems, which were previously intractable or required exponential complexity.
This paper provides a unified framework for solving structural input/output and control configuration selection problems in large-scale systems, achieving global solutions with polynomial complexity algorithms. The framework minimizes the number of manipulated/measured variables for structural controllability/observability and the number of feedback interconnections to avoid structurally fixed modes.
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.