Genetic based fuzzy logic controller for a wall-following mobile robot

Authors:
Sameh F. Desouky;Howard M. Schwartz
Affiliations:
Department of Systems and Computer Engineering, Carleton University, Ottawa, Canada;Department of Systems and Computer Engineering, Carleton University, Ottawa, Canada
Venue:
ACC'09 Proceedings of the 2009 conference on American Control Conference
Year:
2009

Citing 6
Cited 2

Reduction of fuzzy control rules by means of premise learning - method and case study

Fuzzy Sets and Systems - Fuzzy systems
Planning Algorithms

Planning Algorithms
Fuzzy controller with stability and performance rules for nonlinear systems

Fuzzy Sets and Systems
Design of a fuzzy controller in mobile robotics using genetic algorithms

Applied Soft Computing
Adaptive fuzzy control of a class of SISO nonaffine nonlinear systems

Fuzzy Sets and Systems
Tuning of a neuro-fuzzy controller by genetic algorithm

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Hybrid intelligent systems applied to the pursuit-evasion game

SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Self-learning fuzzy logic controllers for pursuit-evasion differential games

Robotics and Autonomous Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper addresses the problem of tuning fuzzy logic controllers. In this paper we presents a new technique called a genetic based fuzzy logic controller (GBFLC). The proposed technique is used to iteratively tune the set of fuzzy logic controller parameters such as membership functions and scaling factors. The proposed technique is also used to reduce the number of fuzzy rules. Computer simulations are performed on a wall-following mobile robot and the results show the usefulness of the proposed technique.