Adaptive Machine Learning Based Control Method for Visual Robot Manipulator System

DING Yaqiong; JIA Hanguang; WAN Kai

doi:10.6054/j.jscnun.2024037

Journal of South China Normal University (Natural Science Edition) > 2024 > 56(3): 50-57. > DOI: 10.6054/j.jscnun.2024037

DING Yaqiong, JIA Hanguang, WAN Kai. Adaptive Machine Learning Based Control Method for Visual Robot Manipulator System[J]. Journal of South China Normal University (Natural Science Edition), 2024, 56(3): 50-57. DOI: 10.6054/j.jscnun.2024037

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Adaptive Machine Learning Based Control Method for Visual Robot Manipulator System

DING Yaqiong¹,
JIA Hanguang^{2, 3, ,},
WAN Kai⁴

1.
School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510006, China
2.
School of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, China
3.
National Key Laboratory of Science and Technology on Reliability Physics and Application of Electronic Component/The Fifth Electronics Research Institute of the Ministry of Industry and Information Technology, Guangzhou 511370, China
4.
School of Electronic Information and Electrical Engineering, Huizhou University, Huizhou 516007, China

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Received Date: May 02, 2024
Available Online: August 16, 2024

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Abstract

Abstract

The control method proposed in this study is based on adaptive machine learning for a multi-input multi-output visual robotic arm system with uncertain control gain matrices. By relaxing the requirement for known control gain matrices in traditional adaptive learning methods, a composite energy function is designed to prove the convergence of the system. The effectiveness of the adaptive iterative learning method is demonstrated through simulating the motion of the robotic arm system and using an uncalibrated camera for validation.
- adaptive machine learning,
- control gain matrix,
- nonlinear system,
- robot manipulator

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