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Removing Feasibility Conditions on Adaptive Neural Tracking Control of Nonlinear Time-Delay Systems With Time-Varying Powers, Input, and Full-State Constraints
期刊论文
IEEE TRANSACTIONS ON CYBERNETICS, 2022, 卷号: 52, 期号: 4, 页码: 2553-2564
作者:
Guo, Chao
;
Xie, Xue-Jun
;
Hou, Zeng-Guang
收藏
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浏览/下载:347/0
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提交时间:2022/06/10
Nonlinear systems
Time-varying systems
Control design
Adaptive systems
Delay effects
Artificial neural networks
Automation
Feasibility conditions
input and full-state constraints
neural networks (NNs)
nonlinear systems
time-varying powers
Fixed-Time Adaptive Fuzzy Control for Uncertain Nonstrict-Feedback Systems with Time-Varying Constraints and Input Saturations
期刊论文
IEEE Transactions on Fuzzy Systems, 2022, 卷号: 30, 期号: 4, 页码: 1114-1128
作者:
Jinlin Su
;
Jianqiang Yi
;
Zhiqiang Pu
Adobe PDF(1378Kb)
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浏览/下载:250/52
  |  
提交时间:2022/04/02
Adaptive fuzzy control
fixed-time control
input saturation
nonstrict-feedback systems
time-varying constraints
Adaptive Critics for Decentralized Stabilization of Constrained-Input Nonlinear Interconnected Systems
期刊论文
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2021, 页码: 13
作者:
Yang, Xiong
;
Zhou, Yingjiang
;
Dong, Na
;
Wei, Qinglai
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浏览/下载:167/0
  |  
提交时间:2022/01/27
Optimal control
Interconnected systems
Nonlinear systems
Decentralized control
Asymptotic stability
Stability analysis
Artificial neural networks
Adaptive critics
adaptive dynamic programming (ADP)
asymmetric input constraints
decentralized control
reinforcement learning (RL)
Neural network-based model predictive tracking control of an uncertain robotic manipulator with input constraints
期刊论文
ISA TRANSACTIONS, 2021, 卷号: 109, 页码: 89-101
作者:
Kang, Erlong
;
Qiao, Hong
;
Gao, Jie
;
Yang, Wenjing
Adobe PDF(942Kb)
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浏览/下载:339/63
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提交时间:2021/03/29
Model predictive control
Neural network
Robotic manipulator
Unknown dynamics
Online learning estimation
Input constraints
Reinforcement Learning-Based Optimal Stabilization for Unknown Nonlinear Systems Subject to Inputs With Uncertain Constraints
期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2020, 卷号: 31, 期号: 10, 页码: 4330-4340
作者:
Zhao, Bo
;
Liu, Derong
;
Luo, Chaomin
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浏览/下载:194/0
  |  
提交时间:2021/01/07
Nonlinear systems
Optimal control
Artificial neural networks
Actuators
Observers
Feedforward systems
Adaptive dynamic programming (ADP)
neural networks (NNs)
optimal control
reinforcement learning (RL)
uncertain input constraints
unknown nonlinear systems
Event-Triggered Adaptive Control of Saturated Nonlinear Systems With Time-Varying Partial State Constraints
期刊论文
IEEE TRANSACTIONS ON CYBERNETICS, 2020, 卷号: 50, 期号: 4, 页码: 1485-1497
作者:
Wang, Lijie
;
Chen, C. L. Philip
;
Li, Hongyi
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浏览/下载:202/0
  |  
提交时间:2020/06/02
Nonlinear systems
Adaptive control
Time-varying systems
Actuators
System performance
Lyapunov methods
Asymmetric
event-triggered adaptive control
input saturation
partial state constraints
Event-Based Constrained Robust Control of Affine Systems Incorporating an AdaptiveCritic Mechanism
期刊论文
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2017, 卷号: 47, 期号: 7, 页码: 1602-1612
作者:
Wang, Ding
;
Mu, Chaoxu
;
Yang, Xiong
;
Liu, Derong
Adobe PDF(1062Kb)
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  |  
浏览/下载:304/94
  |  
提交时间:2017/09/12
Adaptive Critic Mechanism (Acm)
Event-based Control
Input Constraints
Nonlinear Systems
Robust Control
Event-based input-constrained nonlinear H infinity state feedback with adaptive critic and neural implementation
期刊论文
NEUROCOMPUTING, 2016, 卷号: 214, 期号: *, 页码: 848-856
作者:
Wang, Ding
;
Mu, Chaoxu
;
Zhang, Qichao
;
Liu, Derong
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Adobe PDF(1090Kb)
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浏览/下载:346/135
  |  
提交时间:2017/02/14
Adaptive Critic Learning (Acl)
Adaptive Dynamic Programming (Adp)
Event-based Control
Hamilton-jacobi-isaacs (Hji) Equation
Input Constraints
Neural Networks
Nonlinear H-infinity Control
State Feedback
Reinforcement learning for adaptive optimal control of unknown continuous-time nonlinear systems with input constraints
期刊论文
INTERNATIONAL JOURNAL OF CONTROL, 2014, 卷号: 87, 期号: 3, 页码: 553-566
作者:
Yang, Xiong
;
Liu, Derong
;
Wang, Ding
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Adobe PDF(721Kb)
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浏览/下载:614/256
  |  
提交时间:2015/08/12
Adaptive Control
Input Constraints
Neural Networks
Optimal Control
Reinforcement Learning