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Genetic Algorithm Tuned Fuzzy Logic Controller for a Robot Arm with Two-link Flexibility and Two-joint Elasticity

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Abstract

Many recent contributions on flexible link and elastic joint robotic arms focus on how to solve path tracking and vibration damping problems in slow and fast mode control, respectively. For slow mode control, the problem has been dealt with previously by soft computing tools in which some parameters are designed manually. As a result, system performances are often tiresome and intractable. This paper introduces a scheme to improve the system performance by applying genetic algorithms (GAs) to tune the membership function parameters of a fuzzy logic controller for the slow mode of a two-flexible-link and two-elastic-joint robotic manipulator. The system with the new controller is simulated and its behaviour is compared with that provided by conventional and expert-designed fuzzy logic controllers.

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Authors and Affiliations

  1. Department of Automatic Control and Systems Engineering, The University of Sheffield, Sheffield, S1 3JD, UK

    V. B. Nguyen & A. S. Morris

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  1. V. B. Nguyen
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  2. A. S. Morris
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Correspondence to A. S. Morris.

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Nguyen, V.B., Morris, A.S. Genetic Algorithm Tuned Fuzzy Logic Controller for a Robot Arm with Two-link Flexibility and Two-joint Elasticity. J Intell Robot Syst 49, 3–18 (2007). https://doi.org/10.1007/s10846-006-9097-6

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  • DOI: https://doi.org/10.1007/s10846-006-9097-6

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