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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
Authors:  Liu Y.-J.;  Li S.;  Tong S.;  Chen C.L.P.
Favorite | View/Download:7/0 | TC[WOS]:12 TC[Scopus]:0 | Submit date:2019/02/11
Discrete-time systems  neural networks (NNs)  nonlinear systems  optimal control  reinforcement learning  
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
Authors:  Liu, Yan-Jun;  Li, Shu;  Tong, Shaocheng;  Chen, C. L. Philip
Favorite | View/Download:10/0 | TC[WOS]:12 TC[Scopus]:0 | Submit date:2019/01/17
Discrete-time systems  neural networks (NNs)  nonlinear systems  optimal control  reinforcement learning  
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
Authors:  Wen, Guoxing;  Chen, C. L. Philip;  Feng, Jun;  Zhou, Ning
View | Adobe PDF | Favorite | View/Download:916/78 | TC[WOS]:12 TC[Scopus]:0 | Submit date:2018/10/30
Fuzzy logic systems (FLSs)  identifier-actor-critic architecture  multi-agent formation  optimized formation control  reinforcement learning (RL)  
Adaptive quantized fuzzy control of stochastic nonlinear systems with actuator dead-zone Journal article
Information Sciences, 2016,Volume: 370-371,Page: 385-401
Authors:  Wang F.;  Liu Z.;  Zhang Y.;  Chen C.L.P.
Favorite | View/Download:6/0 | TC[WOS]:25 TC[Scopus]:0 | Submit date:2019/02/11
Adaptive fuzzy control  Backstepping technique  Hysteretic quantizer  Stochastic nonlinear quantized systems  Unknown dead zone  
A Unified Approach to Adaptive Neural Control for Nonlinear Discrete-Time Systems With Nonlinear Dead-Zone Input Journal article
IEEE Transactions on Neural Networks and Learning Systems, 2016,Volume: 27,Issue: 1,Page: 139
Authors:  Liu Y.-J.;  Gao Y.;  Tong S.;  Chen C.L.P.
Favorite | View/Download:5/0 | TC[WOS]:68 TC[Scopus]:0 | Submit date:2018/10/30
Adaptive control  discrete Nussbaum gain  discrete-time systems  unknown control direction.  
Adaptive NN controller design for a class of nonlinear MIMO discrete-time systems Journal article
IEEE Transactions on Neural Networks and Learning Systems, 2015,Volume: 26,Issue: 5,Page: 1007
Authors:  Liu Y.-J.;  Tang L.;  Tong S.;  Chen C.L.P.
Favorite | View/Download:9/0 | TC[WOS]:109 TC[Scopus]:0 | Submit date:2018/10/30
Adaptive control  discrete-time  input nonlinearity  neural networks  uncertain nonlinear systems  
Adaptive fuzzy dynamic surface control for a class of nonlinear systems with fuzzy dead zone and dynamic uncertainties Journal article
Nonlinear Dynamics, 2014,Volume: 79,Issue: 3,Page: 1693-1709
Authors:  Wang F.;  Liu Z.;  Zhang Y.;  Chen X.;  Chen C.L.P.
Favorite | View/Download:2/0 | TC[WOS]:19 TC[Scopus]:0 | Submit date:2019/02/11
Adaptive control  Dynamic surface control (DSC)  Dynamic uncertainties  Fuzzy dead zone  Nonlinear systems  
Adaptive neural network control for a DC motor system with dead-zone Journal article
Nonlinear Dynamics, 2013,Volume: 72,Issue: 2018-01-02,Page: 141
Authors:  Liu L.;  Liu Y.-J.;  Chen C.L.P.
Favorite | View/Download:12/0 | TC[WOS]:38 TC[Scopus]:0 | Submit date:2018/10/30
Adaptive control  Barrier Lyapunov function  DC motor system  Nonsymmetric dead-zone  RBFNN