Robust pole assignment for synthesizing feedback control systems using recurrent neural networks

Xinyi Le, Jun Wang

Research output: Contribution to journalArticlepeer-review

64 Citations (Scopus)

Abstract

This paper presents a neurodynamic optimization approach to robust pole assignment for synthesizing linear control systems via state and output feedback. The problem is formulated as a pseudoconvex optimization problem with robustness measure: i.e., the spectral condition number as the objective function and linear matrix equality constraints for exact pole assignment. Two coupled recurrent neural networks are applied for solving the formulated problem in real time. In contrast to existing approaches, the exponential convergence of the proposed neurodynamics to global optimal solutions can be guaranteed even with lower model complexity in terms of the number of variables. Simulation results of the proposed neurodynamic approach for 11 benchmark problems are reported to demonstrate its superiority.

Original languageEnglish
Article number6588322
Pages (from-to)383-393
Number of pages11
JournalIEEE Transactions on Neural Networks and Learning Systems
Volume25
Issue number2
DOIs
Publication statusPublished - Feb 2014
Externally publishedYes

Keywords

  • Pseudoconvexity
  • recurrent neural networks
  • robust pole assignment
  • state and output feedback control
  • state estimation

Fingerprint

Dive into the research topics of 'Robust pole assignment for synthesizing feedback control systems using recurrent neural networks'. Together they form a unique fingerprint.

Cite this