Fuzzy control system performance enhancement by iterative learning control

Radu Emil Precup, Stefan Preitl, József K. Tar, Marius L. Tomescu, Márta Takács, Péter Korondi, Péter Baranyi

Research output: Contribution to journalArticle

72 Citations (Scopus)

Abstract

This paper suggests low-cost fuzzy control solutions that ensure the improvement of control system (CS) performance indices by merging the benefits of fuzzy control and iterative learning control (ILC). The solutions are expressed in terms of three fuzzy CS (FCS) structures that employ ILC algorithms and a unified design method focused on Takagi-Sugeno proportional-integral fuzzy controllers (PI-FCs). The PI-FCs are dedicated to a class of servo systems with linear/linearized controlled plants characterized by second-order dynamics and integral type. The invariant set theorem by Krasovskii and LaSalle with quadratic Lyapunov function candidates is applied to guarantee the convergence of the ILC algorithms and enable proper setting of the PI-FC parameters. The linear PI controller parameters tuned by the extended symmetrical optimum method are mapped onto the PI-FC ones by the modal equivalence principle. Real-time experimental results for a dc-based servo speed CS are included.

Original languageEnglish
Pages (from-to)3461-3475
Number of pages15
JournalIEEE Transactions on Industrial Electronics
Volume55
Issue number9
DOIs
Publication statusPublished - Jan 1 2008

Keywords

  • Fuzzy control
  • Iterative methods
  • Learning control systems
  • Servo systems
  • Stability

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Fuzzy control system performance enhancement by iterative learning control'. Together they form a unique fingerprint.

  • Cite this