Adaptive multivariable fuzzy logic controller

Research output: Contribution to journalArticle

11 Citations (Scopus)

Abstract

This paper presents a systematic methodology to the design of a multivariable fuzzy logic controller (MFLC) for large-scale nonlinear systems. A new general method which is based on a performance index of sliding motion is used to generate a fuzzy control rule base. Reducible input variables obtained from sliding motion are adopted as input variable of the fuzzy controller and the output scale factors of the MFLC are tuned by the switching variable. Thus, the determination of the input/output scale factors becomes easier and the system performance is significantly improved. The simulation results of a Puma 560 system and a two-inverted pendulum system demonstrate that the attractive features of this proposed approach include a smaller residual error and robustness against nonlinear interactions.

Original languageEnglish
Pages (from-to)43-60
Number of pages18
JournalFuzzy Sets and Systems
Volume86
Issue number1
Publication statusPublished - 1997 Jan 1

Fingerprint

Fuzzy Logic Controller
Fuzzy logic
Scale factor
Controllers
Inverted Pendulum
Motion
Rule Base
Nonlinear Interaction
Output
Pendulums
Performance Index
Large-scale Systems
Fuzzy Controller
Fuzzy control
Fuzzy Control
Nonlinear systems
System Performance
Nonlinear Systems
Robustness
Methodology

Keywords

  • Adaptive control
  • Fuzzy control
  • Learning algorithm
  • Sliding mode

ASJC Scopus subject areas

  • Logic
  • Artificial Intelligence

Cite this

Adaptive multivariable fuzzy logic controller. / Yeh, Zong Mu.

In: Fuzzy Sets and Systems, Vol. 86, No. 1, 01.01.1997, p. 43-60.

Research output: Contribution to journalArticle

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