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research-article

Direct Adaptive FAT-Based Control of Robot Manipulators

[+] Author and Article Information
Majid Moradi Zirkohi

Department of Electrical Engineering, Behbahan Khatam Alanbia University of Technology, Behbahan, Iran
moradi@bkatu.ac.ir

1Corresponding author.

ASME doi:10.1115/1.4037269 History: Received July 18, 2016; Revised June 10, 2017

Abstract

In this paper, a simple model-free controller for electrically driven robot manipulators is presented using function approximation techniques (FAT) such as Legendre polynomials (LP) and Foureier series (FS). According to the orthogonal functions theorem, LP and FS can approximate nonlinear functions with an arbitrary small approximation error. From this point of view, they are similar to fuzzy systems and can be used as controller to approximate the ideal control law. In comparison with fuzzy systems and neural networks, LP and FS are simpler and less computational. Moreover, there are very few tuning parameters in LP and FS. Consequently, the proposed controller is less computational in comparison with fuzzy and neural controllers. The case study is an articulated robot manipulator driven by permanent magnet DC motors. Simulation results verify the effectiveness of the proposed control approach and its superiority over neuro-fuzzy controllers.

Copyright (c) 2017 by ASME
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