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Research Papers

Direct Adaptive Function Approximation Techniques Based Control of Robot Manipulators

[+] Author and Article Information
Majid Moradi Zirkohi

Department of Electrical Engineering,
Behbahan Khatam Alanbia University
of Technology,
Behbahan 6361647189, Iran
e-mail: moradi@bkatu.ac.ir

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received July 18, 2016; final manuscript received June 10, 2017; published online September 5, 2017. Assoc. Editor: Azim Eskandarian.

J. Dyn. Sys., Meas., Control 140(1), 011006 (Sep 05, 2017) (11 pages) Paper No: DS-16-1351; doi: 10.1115/1.4037269 History: Received July 18, 2016; Revised June 10, 2017

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 Fourier 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 direct current (DC) motors. Simulation results verify the effectiveness of the proposed control approach and its superiority over neuro-fuzzy controllers.

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Figures

Grahic Jump Location
Fig. 1

The tracking errors using LP

Grahic Jump Location
Fig. 2

The tracking performance using LP

Grahic Jump Location
Fig. 3

Motor voltages using LP

Grahic Jump Location
Fig. 4

The tracking errors using LP in the presence of external disturbance

Grahic Jump Location
Fig. 5

The tracking errors using FS

Grahic Jump Location
Fig. 6

The tracking performance using FS

Grahic Jump Location
Fig. 7

Motor voltages using FS

Grahic Jump Location
Fig. 8

The tracking errors using FS in the presence of external disturbance

Grahic Jump Location
Fig. 9

The tracking errors of fuzzy-neural network

Grahic Jump Location
Fig. 10

The control signals of fuzzy-neural network

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