Research Papers

Fine Tuning of Fuzzy Rule-Base System and Rule Set Reduction Using Statistical Analysis

[+] Author and Article Information
Muhammad Babar Nazir, Shaoping Wang

Department of Mechatronic, School of Automation Sciences and Electrical Engineering, Beihang University, Beijing 100083, China

J. Dyn. Sys., Meas., Control 133(4), 041003 (Apr 06, 2011) (9 pages) doi:10.1115/1.4003376 History: Received June 04, 2009; Revised October 10, 2010; Published April 06, 2011; Online April 06, 2011

Learning and tuning of fuzzy rule-based systems is the core issue for linguistic fuzzy modeling. To achieve an accurate linguistic fuzzy model genetic learning of initial rule base is introduced and evolutionary simultaneous tuning of nonlinear scaling factors and fuzzy membership functions (MFs) are employed. Novel evolutionary algorithm is applied for simultaneous optimization process due to its computational efficiency and reliability. To preserve the interpretability issue, linguistic hedges are utilized, which slightly modify the MFs. Interpretability issue is further improved by introducing the statistical based fuzzy rule reduction technique. In this technique, most appropriate rules are selected by computing the activation tendency of each rule. Further, focusing on granularity of partition, linguistic terms for input and output variables are modified and new reduced rule base system is developed. The proposed techniques are applied to nonlinear electrohydraulic servo system. Extensive simulation and experiment results indicate that proposed schemes not only improve the accuracy but also ensure interpretability preservation. Further, controller developed based on proposed schemes sustains the performance under parametric uncertainties and disturbances.

Copyright © 2011 by American Society of Mechanical Engineers
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Figure 1

Structure of electrohydraulic servo system

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Figure 2

Structure of fuzzy PID system

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Figure 3

Genetic rule base learning for fuzzy PID

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Figure 4

Membership function tuning (a) extended tuning and (b) basic parameters tuning

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Figure 5

Work flow diagram of NEA

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Figure 6

Distribution optimization for error MFs

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Figure 7

Distribution optimization for change error MFs

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Figure 8

Distribution optimization for control MFS

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Figure 9

Optimization curve for NEA

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Figure 10

Comparative analysis of convergence rate for NEA and GA

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Figure 11

Tracking performance before and after optimization

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Figure 12

Tracking performance under different effects

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Figure 13

Tracking performance with reduced rules and different effects

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Figure 14

New membership functions for error

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Figure 15

New membership functions for change error

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Figure 16

New membership functions for control output

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Figure 17

Experiments results for FPID before and after optimization

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Figure 18

Experiments for new rule base under different effects




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