Adaptive neural network control with optimal number of hidden nodes for trajectory tracking of robot manipulators-Reference-Cited by-同舟云学术

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Adaptive neural network control with optimal number of hidden nodes for trajectory tracking of robot manipulators

Published:2019-07 Issue: Volume:350 Page:136-145
ISSN:0925-2312
Container-title:Neurocomputing
language:en
Short-container-title:Neurocomputing

Author:

Liu Chengxiang,Zhao Zhijia,Wen Guilin

Funder

National Natural Science Foundation of China

Innovative School Project of Education Department of Guangdong

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications

Reference42 articles.

1. Adaptive boundary control of an axially moving belt system with high acceleration/deceleration;Zhao;IET Control Theory Appl.,2016

2. Neural-learning-based telerobot control with guaranteed performance;Yang;IEEE Trans. Cybern.,2017

3. Adaptive neural prescribed performance tracking control for near space vehicles with input nonlinearity;Yang;Neurocomputing,2016

4. Dob based neural control of flexible hypersonic flight vehicle considering wind effects;Xu;IEEE Trans. Indust. Electron.,2017

5. Robust adaptive control of an offshore ocean thermal energy conversion system;He;IEEE Trans. Syst. Man Cybern. Syst.,2018

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3. A model-free terminal sliding mode control for robots: Achieving fixed-time prescribed performance and convergence;ISA Transactions;2024-01

4. Adaptive Broad Learning Neural Network for Fault-Tolerant Control of 2-DOF Helicopter Systems;IEEE Transactions on Systems, Man, and Cybernetics: Systems;2023-12

5. Adaptive Tracking Controller with Exponential Restrictions for Robotic Manipulators;2023 11th International Conference on Control, Mechatronics and Automation (ICCMA);2023-11-01

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