A model reference and sensitivity model-based self-learning fuzzy logic controller as a solution for control of nonlinear servo systems

Z. Kovacic, S. Bogdan, M. Balenovic

Research output: Contribution to journalArticleAcademicpeer-review

11 Citations (Scopus)
58 Downloads (Pure)

Abstract

In this paper, the design, simulation and experimental verification of a self-learning fuzzy logic controller (SLFLC) suitable for the control of nonlinear servo systems are described. The SLFLC contains a learning algorithm that utilizes a second-order reference model and a sensitivity model related to the fuzzy controller parameters. The effectiveness of the proposed controller has been tested in the position control loops of two chopper-fed DC servo systems, first by simulation in the presence of a backlash nonlinearity, then by experiment in the presence of a gravity-dependent shaft load and fairly high static friction. The simulation and experimental results have proved that the SLFLC provides desired closed loop behavior and eliminates a steady-state position error
Original languageEnglish
Pages (from-to)1479-1484
Number of pages6
JournalIEEE Transactions on Energy Conversion
Volume14
Issue number4
DOIs
Publication statusPublished - 1999

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