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Collaborative Control for Multimanipulator Systems With Fuzzy Neural Networks

This article develops a fuzzy-neural controller for the kinematic and collaborative control of multimanipulator systems. The entire control scheme is designed based on quadratic programming and implemented by a constructed fuzzy-neural controller. A hybrid minimum joint velocity-acceleration index i...

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Published in:	IEEE transactions on fuzzy systems 2023-04, Vol.31 (4), p.1305-1314
Main Authors:	Zhang, Jiazheng, Jin, Long, Wang, Yang
Format:	Article
Language:	English
Subjects:	Acceleration Artificial neural networks Collaboration Control systems Controllers Energy consumption Fuzzy control Fuzzy logic Fuzzy logic system Fuzzy systems Kinematics Kinetic energy Manipulators multiple manipulators neural networks Quadratic programming Robots Robust control Stability analysis Task analysis Trajectory
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container_title	IEEE transactions on fuzzy systems
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creator	Zhang, Jiazheng Jin, Long Wang, Yang
description	This article develops a fuzzy-neural controller for the kinematic and collaborative control of multimanipulator systems. The entire control scheme is designed based on quadratic programming and implemented by a constructed fuzzy-neural controller. A hybrid minimum joint velocity-acceleration index is introduced to adjust the operating performance of each manipulator and reduce the kinetic energy consumption of the system. Besides, a simple but effective set of membership functions and rules are used to describe the variation of controller parameters caused by the operational complexity and vagueness during task executions. The stability and robustness of the controller are verified through theoretical analysis. Finally, simulations and experimental studies of the multimanipulator system are carried out supporting the practicality of our findings.
doi_str_mv	10.1109/TFUZZ.2022.3198855
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The entire control scheme is designed based on quadratic programming and implemented by a constructed fuzzy-neural controller. A hybrid minimum joint velocity-acceleration index is introduced to adjust the operating performance of each manipulator and reduce the kinetic energy consumption of the system. Besides, a simple but effective set of membership functions and rules are used to describe the variation of controller parameters caused by the operational complexity and vagueness during task executions. The stability and robustness of the controller are verified through theoretical analysis. 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language	eng
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source	IEEE Xplore (Online service)
subjects	Acceleration Artificial neural networks Collaboration Control systems Controllers Energy consumption Fuzzy control Fuzzy logic Fuzzy logic system Fuzzy systems Kinematics Kinetic energy Manipulators multiple manipulators neural networks Quadratic programming Robots Robust control Stability analysis Task analysis Trajectory
title	Collaborative Control for Multimanipulator Systems With Fuzzy Neural Networks
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