engleski

A Model Reference & Sensitivity Model-based Self-learning Fuzzy Logic Controller as a Solution for Control of Nonlinear Servo Systems

In this paper, a design, simulation and experimental verification of a self-learning fuzzy logic controller (SLFLC) suitable for control of nonlinear servo systems is 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 loadand 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.

Fuzzy Systems; Fuzzy Control; Sensitivity Model; Self-Learning Fuzzy Logic Controller; Nonlinear Servo System

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