Information Structure Constraints

Chapter
Part of the Communications and Control Engineering book series (CCE)

Abstract

Large-scale systems require control laws whose computation is efficient, and whose implementation entails a minimal amount of information exchange between the subsystems. In order to design such laws, it is necessary to develop versatile algorithms that can incorporate a broad range of information structure constraints. This problem has received considerable attention in recent years, particularly in the context of Linear Matrix Inequalities (LMIs) (see e.g., Boyd et al., 1994; Geromel et al., 1994; El Ghaoui and Niculescu, 2000; de Oliveira et al., 2000; Ayers and Paganini, 2002; Langbort et al., 2004; Šiljak and Zečević, 2005; Zečević and Šiljak, 2008, and the references therein). In this chapter we will adopt a similar approach, and utilize LMI optimization to obtain structurally constrained control laws that are suitable for complex systems. In doing so, we will rely on the mathematical framework proposed in Šiljak and Stipanović (2000) which will be extended in a number of different ways. We will also take into account the decomposition techniques described in Chap. 1, which suggest that the gain matrix structures shown in Figs. 2.1–2.3 are particularly desirable for large-scale applications.

In the sections that follow, we will describe how each of these structures can be obtained in an efficient manner, and examine a variety of possible applications.

Keywords

Steam Eter Peris Decen 

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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Aleksandar I. Zečević
    • 1
  • Dragoslav D. Šiljak
    • 1
  1. 1.Dept. Electrical EngineeringSanta Clara UniversitySanta ClaraUSA

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