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Constrained-cost adaptive dynamic programming for optimal control of discrete-time nonlinear systems 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2023, 卷号: 35, 期号: 3, 页码: 3251 - 3264
作者:  Wei, Qinglai;  Li, Tao
Adobe PDF(8471Kb)  |  收藏  |  浏览/下载:54/20  |  提交时间:2024/05/28
Adaptive dynamic programming  approximate dynamic programming  constrained cost  optimal control  reinforcement learning  
Adaptive Neural Control of Nonlinear Nonstrict Feedback Systems With Full-State Constraints: A Novel Nonlinear Mapping Method 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2021, 页码: 9
作者:  Zhang, Jiaming;  Niu, Ben;  Wang, Ding;  Wang, Huanqing;  Duan, Peiyong;  Zong, Guangdeng
收藏  |  浏览/下载:231/0  |  提交时间:2022/01/27
Nonlinear systems  Artificial neural networks  Design methodology  Adaptive control  Time-varying systems  Fuzzy logic  Backstepping  Asymptotic tracking control  neural networks (NN)  nonlinear mapping (NM)  nonstrict feedback structure  time-varying full-state constraints  uncertain nonlinear system  
Time-/Event-Triggered Adaptive Neural Asymptotic Tracking Control for Nonlinear Systems With Full-State Constraints and Application to a Single-Link Robot 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2021, 页码: 11
作者:  Zhang, Jiaming;  Niu, Ben;  Wang, Ding;  Wang, Huanqing;  Zhao, Ping;  Zong, Guangdeng
收藏  |  浏览/下载:222/0  |  提交时间:2022/01/27
Artificial neural networks  Nonlinear systems  Control systems  Adaptive systems  Backstepping  Neurons  Task analysis  Asymptotic tracking control  barrier functions  full-state constraints  neural networks (NNs)  time-  event-triggered control  uncertain nonlinear systems  
Adaptive Reinforcement Learning Control Based on Neural Approximation for Nonlinear Discrete-Time Systems With Unknown Nonaffine Dead-Zone Input 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2019, 卷号: 30, 期号: 1, 页码: 295-305
作者:  Liu, Yan-Jun;  Li, Shu;  Tong, Shaocheng;  Chen, C. L. Philip
收藏  |  浏览/下载:339/0  |  提交时间:2019/07/12
Discrete-time systems  neural networks (NNs)  nonlinear systems  optimal control  reinforcement learning