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Recent Progress in Reinforcement Learning and Adaptive Dynamic Programming for Advanced Control Applications 期刊论文
IEEE/CAA Journal of Automatica Sinica, 2024, 卷号: 11, 期号: 1, 页码: 18-36
作者:  Ding Wang;  Ning Gao;  Derong Liu;  Jinna Li;  Frank L. Lewis
Adobe PDF(1945Kb)  |  收藏  |  浏览/下载：234/179  |  提交时间：2024/01/02
Adaptive dynamic programming (ADP)  advanced control  complex environment  data-driven control  event-triggered design  intelligent control  neural networks  nonlinear systems  optimal control  reinforcement learning (RL)  
An Approximate Neuro-Optimal Solution of Discounted Guaranteed Cost Control Design 期刊论文
IEEE TRANSACTIONS ON CYBERNETICS, 2022, 卷号: 52, 期号: 1, 页码: 77-86
作者:  Wang, Ding;  Qiao, Junfei;  Cheng, Long
收藏  |  浏览/下载：221/0  |  提交时间：2022/03/17
Control design  Cost function  Optimal control  Nonlinear systems  Adaptive systems  Switches  Adaptive learning system  discount factor  guaranteed cost function  neuro-optimal control  uncertainty  
Neural robust stabilization via event-triggering mechanism and adaptive learning technique 期刊论文
NEURAL NETWORKS, 2018, 卷号: 102, 页码: 27-35
作者:  Wang, Ding;  Liu, Derong
收藏  |  浏览/下载：182/0  |  提交时间：2018/10/10
Adaptive Critic Designs  Adaptive Dynamic Programming  Event-triggered Control  Neural Networks  Robust Stabilization  Uncertainties  
Neural Network Learning and Robust Stabilization of Nonlinear Systems With Dynamic Uncertainties 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2018, 卷号: 29, 期号: 4, 页码: 1342-1351
作者:  Wang, Ding;  Liu, Derong;  Mu, Chaoxu;  Zhang, Yun
收藏  |  浏览/下载：143/0  |  提交时间：2018/10/10
Adaptive Critic  Dynamical Uncertainty  Learning Systems  Neural Networks  Optimal Control  Robust Stabilization  
Adaptive-Critic-Based Robust Trajectory Tracking of Uncertain Dynamics and Its Application to a Spring-Mass-Damper System 期刊论文
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2018, 卷号: 65, 期号: 1, 页码: 654-663
作者:  Wang, Ding;  Mu, Chaoxu
收藏  |  浏览/下载：148/0  |  提交时间：2019/12/16
Adaptive critic design  neural networks  optimal control  robust trajectory tracking  self-learning control  system uncertainty  
Neural network robust tracking control with adaptive critic framework for uncertain nonlinear systems 期刊论文
NEURAL NETWORKS, 2018, 卷号: 97, 页码: 11-18
作者:  Wang, Ding;  Liu, Derong;  Zhang, Yun;  Li, Hongyi
收藏  |  浏览/下载：201/0  |  提交时间：2018/10/10
Adaptive Critic Designs  Dynamical Uncertainty  Learning Systems  Neural Networks  Optimal Control  Robust Tracking Control  
Event-Based Robust Control for Uncertain Nonlinear Systems Using Adaptive Dynamic Programming 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2018, 卷号: 29, 期号: 1, 页码: 37-50
作者:  Zhang, Qichao;  Zhao, Dongbin;  Wang, Ding
Adobe PDF(2233Kb)  |  收藏  |  浏览/下载：523/239  |  提交时间：2017/05/04
Adaptive Dynamic Programming (Adp)  Event-based Control  Neural Network (Nn)  Robust Control  Unmatched Uncertainties  
Bounded robust control design for uncertain nonlinear systems using single-network adaptive dynamic programming 期刊论文
NEUROCOMPUTING, 2017, 卷号: 266, 页码: 128-140
作者:  Huang, Yuzhu;  Wang, Ding;  Liu, Derong
Adobe PDF(1876Kb)  |  收藏  |  浏览/下载：328/85  |  提交时间：2018/03/03
Neural Networks  Optimal Control  Adaptive Dynamic Programming  Bounded Robust Control  Uncertain Nonlinear Systems  
Robust adaptive critic control design with network-based event-triggered formulation 期刊论文
NONLINEAR DYNAMICS, 2017, 卷号: 90, 期号: 3, 页码: 2023-2035
作者:  Mu, Chaoxu;  Wang, Ding;  Sun, Changyin;  Zong, Qun
Adobe PDF(1014Kb)  |  收藏  |  浏览/下载：377/122  |  提交时间：2018/03/03
Adaptive Critic Control  Disturbance Rejection  Event-triggered Mechanism  Network-based Systems  Robust H-infinity Control  
Adaptive tracking control for a class of continuous-time uncertain nonlinear systems using the approximate solution of HJB equation 期刊论文
NEUROCOMPUTING, 2017, 卷号: 260, 页码: 432-442
作者:  Mu, Chaoxu;  Sun, Changyin;  Wang, Ding;  Song, Aiguo
Adobe PDF(1555Kb)  |  收藏  |  浏览/下载：412/157  |  提交时间：2017/09/12
Adaptive Tracking Control  Hamilton-jacobi-bellman (Hjb) Equation  Adaptive Dynamic Programming (Adp)  Neural Networks  Uncertainties