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Dynamic-horizon model-based value estimation with latent imagination 期刊论文
IEEE Transactions on Neural Networks and Learning Systems, 2022, 页码: 1-14
作者:  Wang JJ(王俊杰);  Zhang QC(张启超);  Zhao DB(赵冬斌)
Adobe PDF(2305Kb)  |  收藏  |  浏览/下载：171/62  |  提交时间：2023/05/30
Latent world model  model-based value expansion (MVE)  reinforcement learning  reinforcement learning  
Data-driven adaptive-critic optimal output regulation towards water level control of boiler-turbine systems 期刊论文
Expert Systems with Applications, 2022, 页码: 117883
作者:  Wei Qinglai;  Wang Xin;  Liu Yu;  Xiong Gang
Adobe PDF(2135Kb)  |  收藏  |  浏览/下载：162/56  |  提交时间：2023/05/23
Velocity and input constrained distributed nash equilibrium seeking for multi-agent integrated game and control via event-triggered communication 期刊论文
NONLINEAR DYNAMICS, 2022, 卷号: 109, 页码: 2781-2798
作者:  Pu, Zhiqiang;  Ai, Xiaolin;  Yi, Jianqiang
Adobe PDF(1775Kb)  |  收藏  |  浏览/下载：283/34  |  提交时间：2022/07/25
Multi-agent systems  Integrated game and control  Velocity constraint  Input saturation  Event-triggered communication  
A New Approach to Finite-Horizon Optimal Control for Discrete-Time Affine Nonlinear Systems via a Pseudolinear Method 期刊论文
IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2022, 卷号: 67, 期号: 5, 页码: 2610-2617
作者:  Wei, Qinglai;  Zhu, Liao;  Li, Tao;  Liu, Derong
Adobe PDF(984Kb)  |  收藏  |  浏览/下载：228/2  |  提交时间：2022/07/25
Time-varying systems  Nonlinear systems  Optimal control  Heuristic algorithms  Dynamic programming  Neural networks  Linear systems  Adaptive dynamic programming  approximate dynamic programming  finite horizon  nonlinear systems  optimal control  pseudolinear approximation  
A novel event-triggered adaptive tracking control framework for a manipulator with aperiodic neural network estimation 期刊论文
Assembly Automation, 2022, 页码: 1-16
作者:  Gao jie
Adobe PDF(2045Kb)  |  收藏  |  浏览/下载：249/83  |  提交时间：2022/06/14
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
Adobe PDF(2838Kb)  |  收藏  |  浏览/下载：220/2  |  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  
Fixed-time consensus disturbance rejection for high-order nonlinear multi-agent systems with input saturation 期刊论文
TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL, 2022, 卷号: 44, 期号: 12, 页码: 2443–2453
作者:  Ai, Xiaolin
Adobe PDF(1670Kb)  |  收藏  |  浏览/下载：186/31  |  提交时间：2022/06/10
Fixed-time consensus  multi-agent systems  disturbance rejection  input saturation  
Tracking of Uncertain Robotic Manipulators Using Event-Triggered Model Predictive Control With Learning Terminal Cost 期刊论文
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING, 2022, 卷号: 19, 期号: 4, 页码: 2801-2815
作者:  Kang, Erlong;  Qiao, Hong;  Chen, Ziyu;  Gao, Jie
Adobe PDF(4203Kb)  |  收藏  |  浏览/下载：442/157  |  提交时间：2022/06/06
Model predictive control  robotic manipulator  leaning terminal cost  neural networks  event-triggered mechanism  unknown dynamics  
Design and Optimization of a Control Framework for Robot Assisted Additive Manufacturing Based on the Stewart Platform 期刊论文
International Journal of Control, Automation and Systems, 2022, 卷号: 20, 期号: 3, 页码: 968-982
作者:  Tamir, Tariku Sinshaw;  Xiong,Gang;  Dong,Xisong;  Fang,Qihang;  Liu,Sheng;  Lodhi,Ehtisham;  Shen,Zhen;  Wang, Fei-Yue
Adobe PDF(3410Kb)  |  收藏  |  浏览/下载：378/175  |  提交时间：2022/04/07
Additive Manufacturing, Extended Proportion-Derivation Sliding Mode Controller, Grey Wolf Optimization, Print Quality, Stewart Platform  
Deep reinforcement learning based multi-target coverage with connectivity guaranteed 期刊论文
IEEE Transactions on Industrial Informatics, 2022, 期号: 2022, 页码: 1-12
作者:  Shiguang Wu;  Zhiqiang Pu;  Tenghai Qiu;  Jianqiang Yi;  Tianle Zhang
Adobe PDF(15731Kb)  |  收藏  |  浏览/下载：270/33  |  提交时间：2022/04/02
Multi-target coverage  multi-robot system  connectivity maintenance  deep reinforcement learning