Neural network-based H∞ tracking control for robotic systems

Y.-C. Chang

doi:10.1049/ip-cta:20000257

Neural network-based H∞ tracking control for robotic systems

IEE Proceedings - Control Theory and Applications ◽

10.1049/ip-cta:20000257 ◽

2000 ◽

Vol 147 (3) ◽

pp. 303-311 ◽

Cited By ~ 36

Author(s):

Y.-C. Chang

Keyword(s):

Neural Network ◽

Tracking Control ◽

Robotic Systems

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Neural network adaptive robust tracking control for uncertain robotic systems with delays

IFAC Proceedings Volumes ◽

10.3182/20080706-5-kr-1001.01983 ◽

2008 ◽

Vol 41 (2) ◽

pp. 11702-11707 ◽

Cited By ~ 1

Author(s):

Yaonan Wang ◽

Yi Zuo ◽

Lihong Huang ◽

Chunsheng Li

Keyword(s):

Neural Network ◽

Tracking Control ◽

Robotic Systems ◽

Robust Tracking ◽

Robust Tracking Control

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Neural network-based tracking control for robotic systems using only position feedback

IEE Proceedings - Control Theory and Applications ◽

10.1049/ip-cta:20010236 ◽

2001 ◽

Vol 148 (1) ◽

pp. 25-34 ◽

Cited By ~ 1

Author(s):

Y.-C. Chang

Keyword(s):

Neural Network ◽

Tracking Control ◽

Robotic Systems ◽

Position Feedback

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Adaptive neural network tracking control of robotic systems

The 2012 International Joint Conference on Neural Networks (IJCNN) ◽

10.1109/ijcnn.2012.6252510 ◽

2012 ◽

Author(s):

Wen-Shyong Yu

Keyword(s):

Neural Network ◽

Tracking Control ◽

Robotic Systems ◽

Adaptive Neural Network

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Adaptive Partial Reinforcement Learning Neural Network-Based Tracking Control for Wheeled Mobile Robotic Systems

IEEE Transactions on Systems Man and Cybernetics Systems ◽

10.1109/tsmc.2018.2819191 ◽

2020 ◽

Vol 50 (7) ◽

pp. 2512-2523 ◽

Cited By ~ 10

Author(s):

Liang Ding ◽

Shu Li ◽

Haibo Gao ◽

Chao Chen ◽

Zongquan Deng

Keyword(s):

Neural Network ◽

Reinforcement Learning ◽

Tracking Control ◽

Partial Reinforcement ◽

Robotic Systems

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Neural Network-Based Hybrid Position/Force Tracking Control for Robotic Systems Without Velocity Measurement

Neural Processing Letters ◽

10.1007/s11063-019-10138-1 ◽

2019 ◽

Vol 51 (2) ◽

pp. 1125-1144

Author(s):

Jinzhu Peng ◽

Shuai Ding ◽

Zeqi Yang ◽

Fangfang Zhang

Keyword(s):

Neural Network ◽

Velocity Measurement ◽

Tracking Control ◽

Robotic Systems ◽

Force Tracking

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An observer-based adaptive neural network tracking control of robotic systems

Applied Soft Computing ◽

10.1016/j.asoc.2013.06.009 ◽

2013 ◽

Vol 13 (12) ◽

pp. 4645-4658 ◽

Cited By ~ 17

Author(s):

Wen-Shyong Yu ◽

Chien-Chih Weng

Keyword(s):

Neural Network ◽

Tracking Control ◽

Robotic Systems ◽

Adaptive Neural Network

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Passivity-based distributed tracking control of uncertain agents via a neural network combined with UDE

Neurocomputing ◽

10.1016/j.neucom.2021.03.008 ◽

2021 ◽

Author(s):

Weihao Li ◽

Kaiyu Qin ◽

Bowen Chen ◽

Boxian Lin ◽

Mengji Shi

Keyword(s):

Neural Network ◽

Tracking Control ◽

Distributed Tracking

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Robust neural network‐based tracking control for unmanned surface vessels under deferred asymmetric constraints

International Journal of Robust and Nonlinear Control ◽

10.1002/rnc.5520 ◽

2021 ◽

Author(s):

Yanchao Sun ◽

Chengpeng Li ◽

Hongde Qin ◽

Zhongchao Deng ◽

Zhe Chen

Keyword(s):

Neural Network ◽

Tracking Control ◽

Surface Vessels

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Neural network for 3D trajectory tracking control of a CMG-actuated underwater vehicle with input saturation

ISA Transactions ◽

10.1016/j.isatra.2021.05.031 ◽

2021 ◽

Author(s):

Ruikun Xu ◽

Guoyuan Tang ◽

De Xie ◽

Lijun Han ◽

Hui Huang

Keyword(s):

Neural Network ◽

Trajectory Tracking ◽

Tracking Control ◽

Input Saturation ◽

Underwater Vehicle ◽

Trajectory Tracking Control

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Neural-network-based stochastic linear quadratic optimal tracking control scheme for unknown discrete-time systems using adaptive dynamic programming

Control Theory and Technology ◽

10.1007/s11768-021-00046-y ◽

2021 ◽

Author(s):

Xin Chen ◽

Fang Wang

Keyword(s):

Neural Network ◽

Dynamic Programming ◽

Discrete Time ◽

Tracking Control ◽

Adaptive Dynamic Programming ◽

Linear Quadratic ◽

Optimal Tracking ◽

Discrete Time Systems ◽

Control Scheme ◽

Time Systems

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