Design of Mamdani fuzzy logic controllers with rule base minimisation using genetic algorithm

Authors:
K. Belarbi;F. Titel;W. Bourebia;K. Benmahammed
Affiliations:
Laboratoire d'Automatique et de Robotique, Faculty of engineering, University of Constantine, Constantine Algeria;Laboratoire d'Automatique et de Robotique, Faculty of engineering, University of Constantine, Constantine Algeria;Laboratoire d'Automatique et de Robotique, Faculty of engineering, University of Constantine, Constantine Algeria;Department of electronics, Faculty of engineering, University of Sétif, Sétif, Algeria
Venue:
Engineering Applications of Artificial Intelligence
Year:
2005

Citing 13
Cited 8

Stability analysis and design of fuzzy control systems

Fuzzy Sets and Systems
On designing a fuzzy control system using an optimization algorithm

Fuzzy Sets and Systems
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)

Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Robust control by fuzzy sliding mode

Automatica (Journal of IFAC)
Extracting fuzzy rules for system modeling using a hybrid of genetic algorithms and Kalman filter

Fuzzy Sets and Systems
Identifying fuzzy models utilizing genetic programming

Fuzzy Sets and Systems
Fuzzy modelling and identification with genetic algorithm based learning

Fuzzy Sets and Systems
On fuzzy logic applications for automatic control, supervision, and fault diagnosis

IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
An approach to fuzzy control of nonlinear systems: stability and design issues

IEEE Transactions on Fuzzy Systems
Designing fuzzy logic controllers using a multiresolutional search paradigm

IEEE Transactions on Fuzzy Systems
Evolutionary design of fuzzy rule base for nonlinear system modeling and control

IEEE Transactions on Fuzzy Systems
Optimal fuzzy controller design: local concept approach

IEEE Transactions on Fuzzy Systems
Simultaneous design of membership functions and rule sets for fuzzycontrollers using genetic algorithms

IEEE Transactions on Fuzzy Systems

Design of robust fuzzy neural network controller with reduced rule base

International Journal of Hybrid Intelligent Systems
Multi-objective optimization of TSK fuzzy models

Expert Systems with Applications: An International Journal
Recent Literature

Fuzzy Sets and Systems
Design of fuzzy logic missile guidance law with minimal rule base

FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 6
Knowledge acquisition in fuzzy-rule-based systems with particle-swarm optimization

IEEE Transactions on Fuzzy Systems
Survey paper: A survey on industrial applications of fuzzy control

Computers in Industry
Backward Q-learning: The combination of Sarsa algorithm and Q-learning

Engineering Applications of Artificial Intelligence
Designing fuzzy rule base using hybrid elite genetic algorithm and tabu search: Application for control and modeling

International Journal of Hybrid Intelligent Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a design procedure for Mamdani fuzzy logic controller including rule base minimisation. The rules are modelled with binary weights on which constraints are imposed in order to ensure consistency. A genetic algorithm is used for finding stabilising controllers that minimise the number of rules. The cost function includes a stability/performance coefficient which insures that stable, performance satisfying controllers are given the highest possible fitness. The number of fuzzy sets for the input and the control variables are set by the user and the design procedure is concerned only with the rule base and the distribution of the fuzzy sets in the universes of discourses. Two examples were studied: the control of the pole and cart system and the control of the concentration in CSTR. In both cases, the fuzzy sets were isosceles triangles evenly distributed, in the universe of discourses.