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2019 | OriginalPaper | Buchkapitel

Adaptive Controller Based on IF-THEN Rules and Simultaneous Perturbation Stochastic Approximation Tuning for a Robotic System

verfasst von : Ludivina Facundo, Chidentree Treesatayapun, Arturo Baltazar

Erschienen in: Advances in Soft Computing

Verlag: Springer International Publishing

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Abstract

This study presents an adaptive controller based on neuro-fuzzy networks and stochastic approximation techniques. The algorithm assumes that the mathematical model of the plant is unknown. An adaptive Fuzzy Rule Emulated Network (FREN) structure is implemented as the main controller. While, a modified version of the Simultaneous Perturbation Stochastic Approximation (SPSA) technique is added as the adaptation algorithm, which estimates the gradient of the plant with respect to the control effort. The proposed FREN+SPSA performance for position control is compared to conventional FREN and classical PID controllers. Experimental tests were performed on a cartesian robotic system, regulating the frequency of a DC motor to follow a desired trajectory. Experimental results show better performance of the proposed FREN+SPSA controller than the conventional FREN and PID controller.

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Vorheriges Kapitel RGB-D Camera and 2D Laser Integration for Robot Navigation in Dynamic Environments

Nächstes Kapitel Multi-objective GA for Collision Avoidance on Robot Manipulators Based on Artificial Potential Field

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Titel: Adaptive Controller Based on IF-THEN Rules and Simultaneous Perturbation Stochastic Approximation Tuning for a Robotic System
verfasst von: Ludivina Facundo
Chidentree Treesatayapun
Arturo Baltazar
Verlag: Springer International Publishing
Buch: Advances in Soft Computing
Print ISBN: 978-3-030-33748-3

Electronic ISBN: 978-3-030-33749-0

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-030-33749-0_54

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