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Consensus Control of Multi-Agent Systems With Two-Way Switching Directed Topology 会议论文
, 北京, 2020-12-5
作者:  Wang Xin;  Wei Qinglai;  Song Ruizhuo
Adobe PDF(898Kb)  |  收藏  |  浏览/下载：105/42  |  提交时间：2023/06/28
Adaptive flocking of multi-agent system with uncertain nonlinear dynamics and unknown disturbances using neural networks 会议论文
, Online, August 20-21
作者:  Shiguang Wu;  Zhiqiang Pu;  Jianqiang Yi;  Jinlin Su;  Tianyi Xiong;  Tenghai Qiu
Adobe PDF(2014Kb)  |  收藏  |  浏览/下载：199/65  |  提交时间：2022/04/06
Continuous-Time Time-Varying Policy Iteration 期刊论文
IEEE TRANSACTIONS ON CYBERNETICS, 2020, 卷号: 50, 期号: 12, 页码: 4958-4971
作者:  Wei, Qinglai;  Liao, Zehua;  Yang, Zhanyu;  Li, Benkai;  Liu, Derong
Adobe PDF(3149Kb)  |  收藏  |  浏览/下载：301/56  |  提交时间：2021/03/02
Optimal control  Nonlinear systems  Time-varying systems  Mathematical model  Dynamic programming  Approximation algorithms  Iterative algorithms  Adaptive critic designs  adaptive dynamic programming (ADP)  neuro-dynamic programming  nonlinear systems  optimal control  policy iteration  
Deep Reinforcement Learning-Based Automatic Exploration for Navigation in Unknown Environment 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2020, 卷号: 31, 期号: 6, 页码: 2064-2076
作者:  Li, Haoran;  Zhang, Qichao;  Zhao, Dongbin
浏览  |  Adobe PDF(4274Kb)  |  收藏  |  浏览/下载：409/125  |  提交时间：2020/08/03
Robot sensing systems  Navigation  Entropy  Neural networks  Task analysis  Planning  Automatic exploration  deep reinforcement learning (DRL)  optimal decision  partial observation  
Event-Triggered Decentralized Tracking Control of Modular Reconfigurable Robots Through Adaptive Dynamic Programming 期刊论文
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2020, 卷号: 67, 期号: 4, 页码: 3054-3064
作者:  Zhao, Bo;  Liu, Derong
收藏  |  浏览/下载：273/0  |  提交时间：2020/03/30
Decentralized control  Couplings  Optimal control  Robots  Dynamic programming  Artificial neural networks  Trajectory  Adaptive dynamic programming  decentralized tracking control  event-triggered mechanism  modular reconfigurable robots  optimal control  reinforcement learning