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Cooperation Control of Under-actuated Mobile Robots with RBF-NN Approximator Conference paper
2018 International Automatic Control Conference (CACS), Taoyuan, Taiwan, 2018-11
作者:  Yu Z.;  Wong S.F.
收藏  |  浏览/下载:5/0  |  提交时间:2019/03/28
Cooperation Control  Lyapunov Direct Method  Rbf Neural Networks  Tracking Control Algorithm  Underactuated Mobile Robot  
Fuzzy Adaptive Compensation Control of Uncertain Stochastic Nonlinear Systems With Actuator Failures and Input Hysteresis Journal article
IEEE TRANSACTIONS ON CYBERNETICS, 2019,Volume: 49,Issue: 1,Page: 2-13
作者:  Wang, Jianhui;  Liu, Zhi;  Chen, C. L. Philip;  Zhang, Yun
收藏  |  浏览/下载:6/0  |  提交时间:2019/01/17
Actuator failure/fault  adaptive control  hysteresis  stochastic nonlinear systems  transient performance  
Adaptive Reinforcement Learning Control Based on Neural Approximation for Nonlinear Discrete-Time Systems With Unknown Nonaffine Dead-Zone Input Journal article
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2019,Volume: 30,Issue: 1,Page: 295-305
作者:  Liu, Yan-Jun;  Li, Shu;  Tong, Shaocheng;  Chen, C. L. Philip
收藏  |  浏览/下载:1/0  |  提交时间:2019/01/17
Discrete-time systems  neural networks (NNs)  nonlinear systems  optimal control  reinforcement learning  
Fuzzy adaptive compensation control of uncertain stochastic nonlinear systems with actuator failures and input hysteresis Journal article
IEEE Transactions on Cybernetics, 2019,Volume: 49,Issue: 1,Page: 2-13
作者:  Wang J.;  Liu Z.;  Chen C.L.P.;  Zhang Y.
收藏  |  浏览/下载:3/0  |  提交时间:2019/02/11
Actuator failure/fault  adaptive control  hysteresis  stochastic nonlinear systems  transient performance  
Adaptive Reinforcement Learning Control Based on Neural Approximation for Nonlinear Discrete-Time Systems with Unknown Nonaffine Dead-Zone Input Journal article
IEEE Transactions on Neural Networks and Learning Systems, 2019,Volume: 30,Issue: 1,Page: 295-305
作者:  Liu Y.-J.;  Li S.;  Tong S.;  Chen C.L.P.
收藏  |  浏览/下载:2/0  |  提交时间:2019/02/11
Discrete-time systems  neural networks (NNs)  nonlinear systems  optimal control  reinforcement learning  
Fuzzy adaptive finite-time control design for nontriangular stochastic nonlinear systems Journal article
IEEE Transactions on Fuzzy Systems, 2019,Volume: 27,Issue: 1,Page: 172-184
作者:  Sui S.;  Chen C.L.P.;  Tong S.
收藏  |  浏览/下载:3/0  |  提交时间:2019/02/11
Multiple-input and multiple-output (MIMO) stochastic nonlinear systems  nontriangular form  state filter  stochastically finite-time control  
Event Trigger Fuzzy Adaptive Compensation Control of Uncertain Stochastic Nonlinear Systems With Actuator Failures Journal article
IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2018,Volume: 26,Issue: 6,Page: 3770-3781
作者:  Liu, Zhi;  Wang, Jianhui;  Chen, C. L. Philip;  Zhang, Yun
收藏  |  浏览/下载:10/0  |  提交时间:2019/01/17
Actuator failure  adaptive control  event trigger  stochastic nonlinear systems  
Asymptotic adaptive control of nonlinear systems with elimination of overparametrization in a Nussbaum-like design Journal article
AUTOMATICA, 2018,Volume: 98,Page: 277-284
作者:  Chen, Ci;  Liu, Zhi;  Xie, Kan;  Zhang, Yun;  Chen, C. L. Philip
收藏  |  浏览/下载:8/0  |  提交时间:2019/01/17
Tuning function  Asymptotic control  Adaptive fuzzy control  Neural network  Computational reduction  
Finite-Time Filter Decentralized Control for Nonstrict-Feedback Nonlinear Large-Scale Systems Journal article
IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2018,Volume: 26,Issue: 6,Page: 3289-3300
作者:  Sui, Shuai;  Tong, Shaocheng;  Chen, C. L. Philip
收藏  |  浏览/下载:5/0  |  提交时间:2019/01/17
Backstepping technique  filter state observer  finite time  nonlinear large-scale systems  nonstrict-feedback form  
Optimized Multi-Agent Formation Control Based on an Identifier-Actor--Critic Reinforcement Learning Algorithm Journal article
IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2018,Volume: 26,Issue: 5,Page: 2719-2731
作者:  Wen, Guoxing;  Chen, C. L. Philip;  Feng, Jun;  Zhou, Ning
浏览  |  Adobe PDF(984Kb)  |  收藏  |  浏览/下载:239/30  |  提交时间:2018/10/30
Fuzzy logic systems (FLSs)  identifier-actor-critic architecture  multi-agent formation  optimized formation control  reinforcement learning (RL)