Neurodynamics-Based Robust Pole Assignment for High-Order Descriptor Systems

Xinyi Le, Jun Wang

Research output: Contribution to journalArticlepeer-review

26 Citations (Scopus)

Abstract

In this paper, a neurodynamic optimization approach is proposed for synthesizing high-order descriptor linear systems with state feedback control via robust pole assignment. With a new robustness measure serving as the objective function, the robust eigenstructure assignment problem is formulated as a pseudoconvex optimization problem. A neurodynamic optimization approach is applied and shown to be capable of maximizing the robust stability margin for high-order singular systems with guaranteed optimality and exact pole assignment. Two numerical examples and vehicle vibration control application are discussed to substantiate the efficacy of the proposed approach.

Original languageEnglish
Article number7244253
Pages (from-to)2962-2971
Number of pages10
JournalIEEE Transactions on Neural Networks and Learning Systems
Volume26
Issue number11
DOIs
Publication statusPublished - 1 Nov 2015
Externally publishedYes

Keywords

  • Descriptor systems
  • high-order systems
  • neurodynamic optimization
  • pseudoconvexity
  • robust pole assignment

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