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1577 results in Control systems and optimization

Page 17 of 79

Data-Driven Identification of Networks of Dynamic Systems
  • Michel Verhaegen, Chengpu Yu, Baptiste Sinquin
  • Published online:
    28 April 2022
    Print publication:
    12 May 2022
  • This comprehensive text provides an excellent introduction to the state of the art in the identification of network-connected systems. It covers models and methods in detail, includes a case study showing how many of these methods are applied in adaptive optics and addresses open research questions. Specific models covered include generic modelling for MIMO LTI systems, signal flow models of dynamic networks and models of networks of local LTI systems. A variety of different identification methods are discussed, including identification of signal flow dynamics networks, subspace-like identification of multi-dimensional systems and subspace identification of local systems in an NDS. Researchers working in system identification and/or networked systems will appreciate the comprehensive overview provided, and the emphasis on algorithm design will interest those wishing to test the theory on real-life applications. This is the ideal text for researchers and graduate students interested in system identification for networked systems.

  • 8 - Discrete-Time Systems: Set-Theoretic Input Uncertainty

  • Alejandro D. Domínguez-García, University of Illinois, Urbana-Champaign
  • Book:
    Large-Scale System Analysis Under Uncertainty
    Published online:
    17 January 2022
    Print publication:
    17 February 2022, pp 237-272

  • Summary

    This chapter analyzes linear and nonlinear discrete-time systems described by a discrete-time state-space model whose inputs are uncertain but known to belong to an ellipsoid. For the linear case, even if the input set is an ellipsoid, the set containing all possible values that the state can take is not an ellipsoid in general, but it can be upper bounded by an ellipsoid. We develop techniques for recursively computing a family of such upper-bounding ellipsoids. Within this family, we then show how to choose ellipsoids that are optimal in some sense, e.g., they have minimum volume. For the nonlinear case, we will again resort to linearization techniques to approximately characterize the set containing all possible values that the state can take. The application of the techniques presented is illustrated using the same inertia-less AC microgrid model used in Chapter 5.

Page 17 of 79