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Research Papers

Improved Neural Network Control Approach for a Humanoid Arm

[+] Author and Article Information
Xinhua Liu

School of Mechatronics Engineering,
China University of Mining and Technology,
Xuzhou 211006, China
e-mail: liuxinhua@cumt.edu.cn

Xiaohui Zhang

School of Mechatronics Engineering,
China University of Mining and Technology,
Xuzhou 211006, China
e-mail: xh_zhang@cumt.edu.cn

Reza Malekian

Department of Computer Science
and Media Technology,
Malmö University,
Malmö 20506, Sweden
e-mail: reza.malekian@ieee.org

Th. Sarkodie-Gyan

College of Engineering,
University of Texas,
500 West University Avenue,
El Paso, TX 79968
e-mail: tsarkodi@utep.edu

Zhixiong Li

School of Engineering,
Ocean University of China,
Tsingdao 266100, China;
School of Mechanical, Materials,
Mechatronic and Biomedical Engineering,
University of Wollongong,
Wollongong, NSW 2522, Australia
e-mail: zhixiong_li@uow.edu.au

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT,AND CONTROL. Manuscript received November 25, 2018; final manuscript received May 7, 2019; published online June 13, 2019. Assoc. Editor: Mohammad A. Ayoubi.

J. Dyn. Sys., Meas., Control 141(10), 101009 (Jun 13, 2019) (13 pages) Paper No: DS-18-1525; doi: 10.1115/1.4043761 History: Received November 25, 2018; Revised May 07, 2019

This study extended the knowledge over the improvement of the control performance for a seven degrees-of-freedom (7DOF) humanoid arm. An improved adaptive Gaussian radius basic function neural network (RBFNN) approach was proposed to ensure the reliability and stability of the humanoid arm control. Considering model uncertainties, the established dynamic model for the humanoid arm was divided into a nominal model and an error model. The error model was approximated by the RBFNN learning to compensate the uncertainties. The contribution of this study mainly concentrates on employing fruit fly optimization algorithm (FOA) to optimize the basic width parameter of the RBFNN, which can enhance the capability of the error approximation speed. Additionally, the output weights of the neural network were adjusted using the Lyapunov stability theory to improve the robustness of the RBFN-based error model. The simulation and experiment results demonstrate that the proposed approach is able to optimize the system state with less tracking errors, regulate the uncertain nonlinear dynamic characteristics, and effectively reduce unexpected interferences.

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Figures

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Fig. 1

Three-dimensional model of the 7DOF humanoid arm and D–H coordinate system

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Fig. 2

Graphic description of the fruit fly group food searching procedure

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Fig. 5

Schematic diagram of the proposed control system

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Fig. 6

FOA optimization curve

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Fig. 7

Tracked trajectories of the joint variable set in the simulation

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Fig. 8

Approximation of f using (a) traditional RBFNN and (b) proposed FOA+RBFNN

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Fig. 9

Control experiment platform of the humanoid arm

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Fig. 10

Tracked trajectory of each joint variable

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Fig. 11

Trajectory tracking errors of the joint variable set

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