A two-stage approach to self-learning direct fuzzy controllers

被引：19

作者：

Pomares, H ^{[1
]}

Rojas, I ^{[1
]}

González, J ^{[1
]}

Rojas, F ^{[1
]}

Damas, M ^{[1
]}

Fernández, FJ ^{[1
]}

机构：

[1] Univ Granada, Fac Ciencias, Dept Comp Architecture & Comp Technol, E-18071 Granada, Spain

来源：

INTERNATIONAL JOURNAL OF APPROXIMATE REASONING | 2002年 / 29卷 / 03期

关键词：

fuzzy logic; adaptive and self-learning fuzzy controllers; on-line learning; direct controllers;

D O I：

10.1016/S0888-613X(01)00068-8

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, a novel approach is presented to fine tune a direct fuzzy controller based on very limited information on the nonlinear plant to be controlled. Without any off-line pretraining, the algorithm achieves very high control performance through a two-stage algorithm. In the first stage, coarse tuning of the fuzzy rules (both rule consequents and membership functions of the premises) is accomplished using the sign of the dependency of the plant output with respect to the control signal and an overall analysis of the main operating regions. In stage two, fine tuning of the fuzzy rules is achieved based on the controller output error using a gradient-based method. The enhanced features of the proposed algorithm are demonstrated by various simulation examples. (C) 2002 Elsevier Science Inc. All rights reserved.

引用

页码：267 / 289

页数：23

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