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PD Control of a Manipulator with Gravity and Inertia Compensation Using an RBF Neural Network

  • Control Theory and Applications
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Abstract

Dynamic compensation can improve the accuracy of trajectory tracking for industrial manipulators. For irregularly shape or flexible manipulators, however, it is difficult to measure the position of the center of mass (COM) so that its dynamic model cannot be expressed explicitly. This paper proposes a proportional derivative (PD) controller with radial basis function neural network based gravity and inertia compensation (RBFNN-GIC). The RBFNN is utilized to estimate the gravity disturbance and to enable identification of COM to calculate the compensated inertia term. The proposed strategy based on the dynamic model can be used on any robot arm whose COM, gravity and inertia are difficult to obtain. To demonstrate the optimization and effectiveness of proposed PD controller, comparative experiments between the proposed control scheme and the traditional data-fitting method least mean square method (LMS) are conducted on a 3 degree of freedom (DOF) robotic manipulator.

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Authors and Affiliations

  1. Electronics Engineering Department, Pusan National University, Busan, Korea

    Yueyuan Zhang, Dongeon Kim, Yudong Zhao & Jangmyung Lee

Authors
  1. Yueyuan Zhang
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  2. Dongeon Kim
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  3. Yudong Zhao
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  4. Jangmyung Lee
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Corresponding author

Correspondence to Jangmyung Lee.

Additional information

This research is based upon work supported by the Ministry of Trade, Industry & Energy (MOTIE, Korea) under Industrial Technology Innovation Program. No.10073147.

Yueyuan Zhang received her B.S. degree in communication engineering from Yanbian University, China and an M.S. degree in electronics engineering from Pusan National University, Korea, in 2017 and 2019, respectively. Now she is an assistant engineer in Bright Dream Robotics. And her research interests include adaptive control theory, path planning and impedance control.

Dongeon Kim received his B.S. degree in electronic engineering from Inje University, Korea, in 2015 and an M.S. degree from Pusan National University, Korea, in 2017. Now he is pursuing a doctoral degree in Pusan National University, Korea, and his research interest includes intelligent control, adaptive control, and machine learning.

Yudong Zhao received his B.S. degree in mechanical design, manufacturing and automation from Henan Polytechnic University, China and an M.S. degree in electronics engineering from Pusan National University, Korea, in 2014 and 2016, respectively. Now he is pursing a doctoral degree in Pusan National University, Korea, and his research interest includes computer vision, adaptive control theory, terminal sliding mode control, and collaboration robotics.

Jangmyung Lee received his B.S. and M.S. degrees in electronics engineering from Seoul National University, Seoul, Korea, in 1980 and 1982, respectively, and his Ph.D. in computer engineering from the University of Southern California (USC), Los Angeles, in 1990. Since 1992, he has been a professor with the Intelligent Robot Laboratory, Pusan National University, Busan, Korea. His current research interests include intelligent robotic systems, ubiquitous ports, and intelligent sensor. Prof. Lee is a past president of the Korean Robotics Society, and a Vice president of ICROS. He is also the head of National Robotics Research Center, SPENALO.

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Zhang, Y., Kim, D., Zhao, Y. et al. PD Control of a Manipulator with Gravity and Inertia Compensation Using an RBF Neural Network. Int. J. Control Autom. Syst. 18, 3083–3092 (2020). https://doi.org/10.1007/s12555-019-0482-x

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  • DOI: https://doi.org/10.1007/s12555-019-0482-x

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