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Adaptive Optimal Discrete-Time Output-Feedback Using an Internal Model Principle and Adaptive Dynamic Programming
期刊论文
IEEE/CAA Journal of Automatica Sinica, 2024, 卷号: 11, 期号: 1, 页码: 131-140
作者:
Zhongyang Wang
;
Youqing Wang
;
Zdzisław Kowalczuk
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浏览/下载：0/0
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提交时间：2024/01/02
Adaptive dynamic programming (ADP)
internal model principle (IMP)
output feedback problem
policy iteration (PI)
value iteration (VI)
Multistep Look-Ahead Policy Iteration for Optimal Control of Discrete-Time Nonlinear Systems With Isoperimetric Constraints
期刊论文
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2023, 页码: 13
作者:
Li, Tao
;
Wei, Qinglai
;
Wang, Fei-Yue
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浏览/下载：0/0
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提交时间：2024/02/22
Performance analysis
Optimal control
Dynamic programming
Iterative algorithms
Upper bound
Measurement
Convergence
Adaptive dynamic programming (ADP)
isoperimetric constraints
nonlinear systems
optimal control
policy iteration
Data-Driven Optimal Output Cluster Synchronization Control of Heterogeneous Multi-Agent Systems
期刊论文
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING, 2023, 页码: 11
作者:
Li, Hongyang
;
Wei, Qinglai
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浏览/下载：6/0
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提交时间：2023/11/17
Index Terms- Output cluster synchronization control
data-driven control
adaptive dynamic programming
policy iteration
heterogeneous multi-agent systems
optimal control
Continuous-Time Stochastic Policy Iteration of Adaptive Dynamic Programming
期刊论文
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2023, 页码: 13
作者:
Wei, Qinglai
;
Zhou, Tianmin
;
Lu, Jingwei
;
Liu, Yu
;
Su, Shuai
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浏览/下载：4/0
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提交时间：2023/11/17
Adaptive dynamic programming (ADP)
Hamilton-Jacobi-Bellman equation (HJBE)
nonlinear stochastic system
stochastic policy iteration (PI)
A Novel Data-Based Fault-Tolerant Control Method for Multicontroller Linear Systems via Distributed Policy Iteration
期刊论文
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2022, 页码: 11
作者:
Wei, Qinglai
;
Li, Hongyang
;
Li, Tao
;
Wang, Fei-Yue
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浏览/下载：12/0
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提交时间：2023/02/22
Fault tolerant systems
Fault tolerance
Linear systems
Control systems
Actuators
Optimal control
Mathematical models
Distributed policy iteration
fault-tolerant control
multicontroller linear systems
optimal control
Online Minimax Q Network Learning for Two-Player Zero-Sum Markov Games
期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2022, 卷号: 33, 期号: 3, 页码: 1228-1241
作者:
Zhu, Yuanheng
;
Zhao, Dongbin
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浏览/下载：15/0
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提交时间：2022/06/10
Games
Nash equilibrium
Mathematical model
Markov processes
Convergence
Dynamic programming
Training
Deep reinforcement learning (DRL)
generalized policy iteration (GPI)
Markov game (MG)
Nash equilibrium
Q network
zero sum
A New Integral Critic Learning for Optimal Tracking Control with Applications to Boiler-Turbine Systems
期刊论文
OPTIMAL CONTROL APPLICATIONS & METHODS, 2021, 页码: 16
作者:
Wei, Qinglai
;
Liu, Yujia
;
Lu, Jingwei
;
Ling, Jun
;
Luan, Zhenhua
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浏览/下载：26/0
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提交时间：2021/12/28
adaptive dynamic programming
boiler-turbine system
integral reinforcement learning
neural network
policy iteration
Continuous-Time Distributed Policy Iteration for Multicontroller Nonlinear Systems
期刊论文
IEEE TRANSACTIONS ON CYBERNETICS, 2021, 卷号: 51, 期号: 5, 页码: 2372-2383
作者:
Wei, Qinglai
;
Li, Hongyang
;
Yang, Xiong
;
He, Haibo
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浏览/下载：63/0
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提交时间：2021/06/07
Optimal control
Nonlinear systems
Decentralized control
Mathematical model
Convergence
Multi-agent systems
Adaptive dynamic programming (ADP)
approximate dynamic programming
distributed policy iteration
nonlinear systems
optimal control
A partial policy iteration ADP algorithm for nonlinear neuro-optimal control with discounted total reward
期刊论文
NEUROCOMPUTING, 2021, 卷号: 424, 页码: 23-34
作者:
Liang, Mingming
;
Wei, Qinglai
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浏览/下载：39/0
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提交时间：2021/03/15
Adaptive critic designs
Adaptive dynamic programming
Policy iteration
Neural networks
Neuro-dynamic programming
Nonlinear systems
Optimal control
Multiagent Reinforcement Learning:Rollout and Policy Iteration
期刊论文
IEEE/CAA Journal of Automatica Sinica, 2021, 卷号: 8, 期号: 2, 页码: 249-272
作者:
Dimitri Bertsekas
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浏览/下载：20/0
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提交时间：2021/04/09
Dynamic programming
multiagent problems
neuro-dynamic programming
policy iteration
reinforcement learning, rollout
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