Using a genetic algorithm to fully optimise a fuzzy logic controller for a two-link-flexible robot arm

Authors:
V. b. Nguyen;A. s. Morris
Affiliations:
Department of automatic control and systems engineering, university of sheffield, uk.;Department of automatic control and systems engineering, university of sheffield, uk.
Venue:
Robotica
Year:
2009

Citing 12
Cited 0

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Robotics: control, sensing, vision, and intelligence
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Adaptation in natural and artificial systems
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Fuzzy Sets and Systems
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Intelligent Control: Aspects of Fuzzy Logic and Neural Nets
Quadratic optimal control of a two-flexible-link robot manipulator

Robotica
Robust Control of a Two-Link Flexible Manipulator with Quasi-Static Deflection Compensation Using Neural Networks

Journal of Intelligent and Robotic Systems
Fuzzy learning control for a flexible-link robot

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

IEEE Transactions on Fuzzy Systems
Mathematical modeling and fuzzy control of a flexible-link robot arm

Mathematical and Computer Modelling: An International Journal

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Abstract

Flexible manipulators have received wide attention because they surpass their rigid counterparts in many criteria. Unfortunately, traditional controller design methods for flexible arms implemented by human experts are usually tedious and intractable. In order to improve system behaviour, this paper proposes schemes based on genetic algorithms (GAs) which optimise the parameters of a fuzzy logic controller for a robotic manipulator with two-link flexibility and two-joint elasticity. Two alternative GA-optimised schemes are simulated and their control behaviour is compared with that of human-expert-designed controller.