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Sökning: L773:0022 0434 OR L773:1528 9028 > (2015-2019) > Improved Neural Net...

Improved Neural Network Control Approach for a Humanoid Arm

Xinhua, Liu (författare)
School of Mechatronics Engineering, China University of Mining and Technology, Xuzhou, 211006, China
Xiaohui, Zhang (författare)
School of Mechatronics Engineering, China University of Mining and Technology, Xuzhou, 211006, China
Malekian, Reza, 1983- (författare)
Malmö universitet,Institutionen för datavetenskap och medieteknik (DVMT)
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Th., Sarkodie-Gyan (författare)
College of Engineering, University of Texas, El Paso, 79968, TX, United States
Zhixiong, Li (författare)
School of Engineering, Ocean University of China; Tsingdao, 266100, China; School of Mechanical, Materials, Mechatronic and Biomedical Engineering, University of Wollongong, Wollongong, 2522, NSW, Australia
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 (creator_code:org_t)
2019-06-13
2019
Engelska.
Ingår i: Journal of Dynamic Systems Measurement, and Control. - : ASME Press. - 0022-0434 .- 1528-9028. ; 141:10, s. 1-13
  • Tidskriftsartikel (refereegranskat)
Abstract Ämnesord
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  • 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.

Nyckelord

adaptive control
fruit fly optimization algorithm
humanoid arm radial basis function network

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