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Optimized Multi-Agent Formation Control Based on an Identifier-Actor--Critic Reinforcement Learning Algorithm 期刊论文
IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2018, 卷号: 26, 期号: 5, 页码: 2719-2731
作者:  Wen, Guoxing;  Chen, C. L. Philip;  Feng, Jun;  Zhou, Ning
收藏  |  浏览/下载：299/0  |  提交时间：2019/12/16
Fuzzy logic systems (FLSs)  identifier-actor-critic architecture  multi-agent formation  optimized formation control  reinforcement learning (RL)  
Neural-network-based online optimal control for uncertain non-linear continuous-time systems with control constraints 期刊论文
IET CONTROL THEORY AND APPLICATIONS, 2013, 卷号: 7, 期号: 17, 页码: 2037-2047
作者:  Yang, Xiong;  Liu, Derong;  Huang, Yuzhu
浏览  |  Adobe PDF(493Kb)  |  收藏  |  浏览/下载：374/96  |  提交时间：2015/08/12
Adaptive Control  Approximation Theory  Closed Loop Systems  Continuous Time Systems  Lyapunov Methods  Neurocontrollers  Nonlinear Control Systems  Optimal Control  Robust Control  Uncertain Systems  Neural Network-based Online Adaptive Optimal Control  Uncertain Nonlinear Continuous-time Systems  Control Constraints  Infinite-horizon Optimal Control Problem  Control Policy  Saturation Constraints  Identifier-critic Architecture  Hamilton-jacobi-bellman Equation Approximation  Uncertain System Dynamics  Critic Nn  Action-critic Dual Networks  Reinforcement Learning  Identifier Nn  Policy Iteration  Lyapunovaeuros Direct Method  Closed Loop System Stability