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Online optimal consensus control of unknown linear multi-agent systems via time-based adaptive dynamic programming Journal article
Liu,Yifan, Li,Tieshan, Shan,Qihe, Yu,Renhai, Wu,Yue, Chen,C. L.Philip. Online optimal consensus control of unknown linear multi-agent systems via time-based adaptive dynamic programming[J]. NEUROCOMPUTING, 2020, 404, 137-144.
Authors:  Liu,Yifan;  Li,Tieshan;  Shan,Qihe;  Yu,Renhai;  Wu,Yue; et al.
Favorite | TC[WOS]:22 TC[Scopus]:25  IF:5.5/5.5 | Submit date：2021/03/09
Adaptive Dynamic Programming (Adp)  Consensus Control  Discrete-time (Dt) System  Linear Multi-agent Systems (Mass)  
Adaptive NN event-triggered control for path following of underactuated vessels with finite-time convergence Journal article
Li, Meilin, Li, Tieshan, Gao, Xiaoyang, Shan, Qihe, Chen, C. L.Philip, Xiao, Yang. Adaptive NN event-triggered control for path following of underactuated vessels with finite-time convergence[J]. Neurocomputing, 2020, 379, 203-213.
Authors:  Li, Meilin;  Li, Tieshan;  Gao, Xiaoyang;  Shan, Qihe;  Chen, C. L.Philip; et al.
Favorite | TC[WOS]:72 TC[Scopus]:83  IF:5.5/5.5 | Submit date：2021/12/03
Event-triggered Control  Finite-time Convergence  Line-of-sight  Path Following  Radial Basis Function Neural Network  Underactuated Marine Surface Vessel  
Consensus of multi-agent systems with impulsive perturbations and time-varying delays by dynamic delay interval method Journal article
Shan, Qihe, Chen, Zengzhi, Li, Tieshan, Philip Chen, C. L.. Consensus of multi-agent systems with impulsive perturbations and time-varying delays by dynamic delay interval method[J]. Communications in Nonlinear Science and Numerical Simulation, 2019, 78, 104890.
Authors:  Shan, Qihe;  Chen, Zengzhi;  Li, Tieshan;  Philip Chen, C. L.
Favorite | TC[WOS]:13 TC[Scopus]:14  IF:3.4/3.3 | Submit date：2022/05/17
Consensus  Delayed Multi-agent System  Dynamic Delay Interval Method  Impulsive Perturbation  
A Fast Method of Function Approximation using Broad Learning System Conference paper
Ma, Yuzhuo, Li, Tieshan, Zuo, Yi, Philip Chen, C. L., Yuan, Liang'en, Shan, Qihe. A Fast Method of Function Approximation using Broad Learning System[C], 2019, 84-88.
Authors:  Ma, Yuzhuo;  Li, Tieshan;  Zuo, Yi;  Philip Chen, C. L.;  Yuan, Liang'en; et al.
Favorite | TC[Scopus]:0 | Submit date：2022/05/17
Bpnn  Broad Learning System  Function Approximation  Rbfnn  
A review of research on port throughput forecasting Conference paper
Yiying Li, Tieshan Li, Yi Zuo, C. L. Philip Chen, Qihe Shan, Yang Xiao, Xiaoqing Fan. A review of research on port throughput forecasting[C]:IEEE, 2019, 449-453.
Authors:  Yiying Li;  Tieshan Li;  Yi Zuo;  C. L. Philip Chen;  Qihe Shan; et al.
Favorite | TC[Scopus]:5 | Submit date：2021/03/09
Nonlinear Model  Port Throughput  Qualitative Analysis  Quantitative Analysis  Time Series Prediction  
Adaptive leader-following formation control with collision avoidance for a class of second-order nonlinear multi-agent systems Journal article
Shi, Quan, Li, Tieshan, Li, Jingqi, Chen, C. L.Philip, Xiao, Yang, Shan, Qihe. Adaptive leader-following formation control with collision avoidance for a class of second-order nonlinear multi-agent systems[J]. Neurocomputing, 2019, 350, 282-290.
Authors:  Shi, Quan;  Li, Tieshan;  Li, Jingqi;  Chen, C. L.Philip;  Xiao, Yang; et al.
Favorite | TC[WOS]:108 TC[Scopus]:133  IF:5.5/5.5 | Submit date：2022/05/17
Artificial Potential Field Method  Collision Avoidance  Formation Control  Neural-network  Second-order Nonlinear Multi-agent Systems  
Containment Control of Multi-Agent Systems with General Noise Based on Hierarchical Topology Reconfiguration Journal article
Shan, Qihe, Yan, Jingyu, Li, Tieshan, Philip Chen, C. L.. Containment Control of Multi-Agent Systems with General Noise Based on Hierarchical Topology Reconfiguration[J]. IEEE Access, 2019, 7, 56826-56838.
Authors:  Shan, Qihe;  Yan, Jingyu;  Li, Tieshan;  Philip Chen, C. L.
Favorite | TC[WOS]:12 TC[Scopus]:12  IF:3.4/3.7 | Submit date：2022/05/23
Containment Control  General Noise  Hierarchical Topology Reconfiguration  Multi-agent Systems