文国兴简介

来源：3003com必赢发布时间：2020-07-19浏览次数：4525

一、基本情况

文国兴，男，1977年2月出生，博士研究生，教授，滨州市五一劳动奖章获得者，山东省有突出贡献的中青年专家。主讲课程：高等数学、概率统计、计算方法等课程。

2011年4月获得辽宁工业大学理学硕士学位，2014年11月获得澳门大学-科技学院博士学位，2015年9月-2016年9月，在新加坡国立大学从事博士后工作。主要研究兴趣集中在：非线性系统自适应控制、多智能体控制、优化控制、强化学习、神经网络、模糊系统等领域。近年来，以第一作者兼通讯作者身份发表SCI学术论文20余篇，其中2篇被收录为全球ESI高被引论文，13篇发表在中科院分区一区期刊IEEE Transaction系列期刊，2篇发表在中科院分区二区期刊。以合作作者身份发表SCI学术论文20余篇，4篇收录为全球ESI高被引论文。

近年来，主持山东省教育厅人才项目，获得资助经费200万元；主持山东省自然科学基金面上项目1项，获得资助经费14万元；2019年12月获得山东省高等学校优秀科研成果奖三等奖1项，位次1/2；2017年12月获得滨州市自然科学优秀学术成果奖一等奖1项，位次1/3。

二、职称晋升

1. 2021.12-至今教授

2. 2019.12-2021.12 副教授

3. 2016.9-2019.12 讲师

三、荣誉称号

2020.5 授予单位：滨州市总工会授予称号：滨州市“五一劳动奖章”获得者
2020.2-2025.1 授予单位：山东省人民政府授予称号：山东省有突出贡献的中青年专家
2019.6-2022.6 授予单位：滨州学院授予称号：校聘教授，
2018.3-2021.2 授予单位：滨州学院授予称号：“聚英计划”，第二层次，

四、获奖情况

文国兴（1/2）; 非线性多智能体的一致控制，山东省高等学校优秀科研成果奖，三等奖，2019.12(文国兴，冯君)；
文国兴（1/1）; Optimized Backstepping for Tracking Control of Strict Feedback Systems，2019年度滨州学院优秀科研成果奖，二等奖， 2019.6（文国兴）；
文国兴（1/2）；Neural network-based adaptive leader-following consensus control for a class of nonlinear multiagent state-delay systems， 2018年滨州学院优秀科研成果奖，二等奖， 2018.5（文国兴，陈俊龙）；
文国兴（1/3）; 基于神经网络的二阶非线性多智能体系统的自适应领导-跟随者一致控制, 滨州市十三届自然科学优秀学术成果奖，一等奖， 2017.12（文国兴，陈俊龙，刘艳军）；

五、主持项目

国家级项目，国家自然科学基金（面上项目），非线性严格反馈系统的自适应强化学习优化控制，基金号：62073045，资助金额：58万，位次：1/8, 日期： 2021.1-2024.12
山东省教育厅人才项目，山东省高等学校青年创新团队，无人机的优化自主控制研究创新团队，资助金额：200万，位次：学科带头人, 日期： 2019.10-2021.12
省级项目，山东省自然科学基金（面上项目），多智能体编队的优化控制，基金号：ZR2018MF015，资助金额：14万，位次：1/7, 日期： 2018.3-2020.12
校级，滨州学院博士启动基金，非线性多智能体的一致控制，基金号：2016Y14，资助金额：20万，位次：1/4, 日期： 2016.3-2019.2

六、代表性论著

Guoxing Wen*, Bin Li, “Optimized Leader-Follower Consensus Control Using Reinforcement Learning for a Class of Second-Order Nonlinear Multi-Agent Systems”, IEEE Transactions on Systems, Man and Cybernetics: Systems, vol. , no. , pp. , DOI: 10.1109/TSMC.2021.3130070.
Guoxing Wen*, Wei Hao, Weiwei Feng and Kaizhou Gao, “Optimized Backstepping Tracking Control Using Reinforcement Learning for Quadrotor Unmanned Aerial Vehicle System”, IEEE Transactions on Systems, Man and Cybernetics: Systems, vol. , no. , pp. , DOI: 10.1109/TSMC.2021.3112688.
Guoxing Wen^*, C. L. Philip Chen, “Optimized Backstepping Consensus Control Using Reinforcement Learning for a Class of Nonlinear Strict-Feedback-Dynamic Multi-Agent Systems”, IEEE Transactions on Neural Networks and Learning Systems, vol. , no. , pp. , DOI: 10.1109/TNNLS.2021.3105548.
Guoxing Wen^*, Liguang Xu, Bin Li, “Optimized Backstepping Tracking Control Using Reinforcement Learning for a Class of Stochastic Nonlinear Strict-Feedback Systems”, IEEE Transactions on Neural Networks and Learning Systems, vol. , no. , pp. , DOI: 10.1109/TNNLS.2021.3105176.
Guoxing Wen*, C. L. Philip Chen, Shuzhi Sam Ge, “Simplified Optimized Backstepping Control for a Class of Nonlinear Strict-Feedback Systems with Unknown Dynamic Functions”, IEEE Transactions on Cybernetics, vol. 51, no. 9, pp. 4567-4580, Sept. 2021, DOI: 10.1109/TCYB.2020.3002108.
Guoxing Wen^*, Chenyang Zhang, Ping Hu, Yang Cui, "Adaptive Neural Network Leader-Follower Formation Control for a Class of Second-Order Nonlinear Multi-Agent Systems with Unknown Dynamics," in IEEE Access, vol. 8, pp. 148149-148156, Oct. 2020, DOI: 10.1109/ACCESS.2020.3015957.
Guoxing Wen^*, C. L. Philip Chen, Bin Li, “Optimized Formation Control Using SimplifiedReinforcement Learning for a Class ofMultiagent Systems with Unknown Dynamics”,IEEE Transactions on Industrial Electronics, vol. 67, no. 9, pp. 7879 – 7888, Sept. 2020, DOI:10.1109/TIE.2019.2946545.
Guoxing Wen^*, C. L. Philip Chen, Wei Nian Li, “Simplified optimized control using reinforcement learning algorithm for a class of stochastic nonlinear systems”, Information Sciences, vol. 517, pp. 230–243, May 2020, DOI: 10.1016/j.ins.2019.12.039.
Guoxing Wen^*, C. L. Philip Chen, Shuzhi Sam Ge, Hongli Yang, Xiaoguang Liu “Optimized adaptive nonlinear tracking control using actor-critic reinforcement learning strategy”, IEEE Transactions on Industrial Informatics, vol. 15, no. 9, pp. 4969-4977, Sep. 2019, DOI: 10.1109/TII.2019.2894282.
Guoxing Wen^*, Shuzhi Sam Ge, C. L. Philip Chen, Fangwen Tu, Shengnan Wang, “AdaptiveTracking Control of Surface Vessel Using Optimized Backstepping Technique”, IEEE Transactions on Cybernetics, vol. 49, no. 9, pp. 3420 - 3431, Sep. 2019, DOI: 10.1109/TCYB.2018.2844177.
Guoxing Wen^*, C. L. Philip Chen, Hui Dou, Hongli Yang, Chunfang Liu “Formation Control with Obstacle Avoidance of Second-Order Multi-Agent Systems under Directed Communication Topology”, Science China Information Science, vol. 62, no. 9, pp. 192205:1-192205:14, July 2019, DOI:CNKI:SUN:JFXG.0.2019-09-011.
Guoxing Wen^*, C. L. Philip Chen, Jun. Feng and Ning. Zhou, "Optimized Multi-Agent Formation Control Based on Identifier-Actor-Critic Reinforcement Learning Algorithm", in IEEE Transactions on Fuzzy Systems, vol. 26, no. 5, pp.2719 - 2731, Oct. 2018, DOI: 10.1109/TFUZZ.2017.2787561.
Guoxing Wen^*, Shuzhi Sam Ge, Fangwen Tu, "Optimized Backstepping for Tracking Control of Strict Feedback Systems”, IEEE Transactions on Neural Networks and Learning Systems, vol. 28, no. 8, pp. 3850-3862, Aug. 2018, DOI: 10.1109/TNNLS.2018.2803726.
Guoxing Wen^*, C. L. Philip Chen, Yan-Jun Liu, "Formation Control with Obstacle Avoidance for a class of Stochastic Multi-Agent Systems", IEEE Transactions on Industrial Electronics, vol. 65, no. 7, pp. 5847-5855, Jul. 2018, DOI: 10.1109/TIE.2017.2782229.
Guoxing Wen^*, C. L. Philip Chen, Yan-Jun Liu, Zhi Liu, “Neural-Network-Based Adaptive Leader-Following Consensus Control for a Class of Nonlinear Multi-Agent State-Delay Systems”, IEEE Transactions on Cybernetics, vol. 47, no. 8, pp. 2151-2160, Aug. 2017, DOI: 10.1109/TCYB.2016.2608499（高被引论文）.
Guoxing Wen^*, Shuzhi Sam Ge, Fangwen Tu, “Artificial Potential-Based Adaptive H∞ Synchronized Tracking Control for Accommodation Vessel”, IEEE Transactions on Industrial Electronics, vol. 64, no. 7, pp. 5640-5647, July 2017, DOI: 10.1109/TIE.2017.2677330.
Guo-Xing Wen^*, C. L. Philip Chen, Yan-Jun Liu, Zhi Liu, “Neural-network-based adaptive leader-following consensus control for second-order non-linear multi-agent systems”, IET Control Theory & Applications, Vol. 13, no. 9, pp. 1927-1934, Aug. 2015, DOI: 10.1049/iet-cta.2014.1319（高被引论文）.
Guo-Xing Wen, Yan-Jun Liu and C. L. Philip Chen, “Direct adaptive robust NN control for a class of discrete-time nonlinear strict-feedback SISO systems”, Neural Computing and Applications, Vol. 21, No.6, pp.1423-1431, Sep. 2012, DOI: 10.1007/s00521-011-0596-4.
Guo-Xing Wen, Yan-Jun Liu, Adaptive Fuzzy-Neural Tracking Control for Uncertain Non-linear Discrete-Time Systems in the NARMAX form, Nonlinear Dynamics, Vol. 66, No. 4, pp. 745-753, Feb. 2011, DOI: 10.1007/s11071-011-9947-z.
Guo-Xing Wen, Yan-Jun Liu, Shao-Cheng Tong, Xiao-Li Li, Adaptive neural output feedback control of nonlinear discrete-time systems, Nonlinear Dynamics, Vol.65, No.1-2, pp.65-75, Nov. 2010, DOI: 10.1007/s11071-010-9874-4.

七、联系方式

E-mail：wengx_bzu@hotmail.com

文国兴简介

Guoxing Wen*, Bin Li, “Optimized Leader-Follower Consensus Control Using Reinforcement Learning for a Class of Second-Order Nonlinear Multi-Agent Systems”, IEEE Transactions on Systems, Man and Cybernetics: Systems, vol. , no. , pp. , DOI: 10.1109/TSMC.2021.3130070.

Guoxing Wen*, Wei Hao, Weiwei Feng and Kaizhou Gao, “Optimized Backstepping Tracking Control Using Reinforcement Learning for Quadrotor Unmanned Aerial Vehicle System”, IEEE Transactions on Systems, Man and Cybernetics: Systems, vol. , no. , pp. , DOI: 10.1109/TSMC.2021.3112688.

Guoxing Wen*, C. L. Philip Chen, “Optimized Backstepping Consensus Control Using Reinforcement Learning for a Class of Nonlinear Strict-Feedback-Dynamic Multi-Agent Systems”, IEEE Transactions on Neural Networks and Learning Systems, vol. , no. , pp. , DOI: 10.1109/TNNLS.2021.3105548.

Guoxing Wen*, Liguang Xu, Bin Li, “Optimized Backstepping Tracking Control Using Reinforcement Learning for a Class of Stochastic Nonlinear Strict-Feedback Systems”, IEEE Transactions on Neural Networks and Learning Systems, vol. , no. , pp. , DOI: 10.1109/TNNLS.2021.3105176.

Guoxing Wen*, C. L. Philip Chen, Shuzhi Sam Ge, “Simplified Optimized Backstepping Control for a Class of Nonlinear Strict-Feedback Systems with Unknown Dynamic Functions”, IEEE Transactions on Cybernetics, vol. 51, no. 9, pp. 4567-4580, Sept. 2021, DOI: 10.1109/TCYB.2020.3002108.

Guoxing Wen*, Chenyang Zhang, Ping Hu, Yang Cui, "Adaptive Neural Network Leader-Follower Formation Control for a Class of Second-Order Nonlinear Multi-Agent Systems with Unknown Dynamics," in IEEE Access, vol. 8, pp. 148149-148156, Oct. 2020, DOI: 10.1109/ACCESS.2020.3015957.

Guoxing Wen*, C. L. Philip Chen, Bin Li, “Optimized Formation Control Using SimplifiedReinforcement Learning for a Class ofMultiagent Systems with Unknown Dynamics”,IEEE Transactions on Industrial Electronics, vol. 67, no. 9, pp. 7879 – 7888, Sept. 2020, DOI:10.1109/TIE.2019.2946545.

Guoxing Wen*, C. L. Philip Chen, Wei Nian Li, “Simplified optimized control using reinforcement learning algorithm for a class of stochastic nonlinear systems”, Information Sciences, vol. 517, pp. 230–243, May 2020, DOI: 10.1016/j.ins.2019.12.039.

Guoxing Wen*, C. L. Philip Chen, Shuzhi Sam Ge, Hongli Yang, Xiaoguang Liu “Optimized adaptive nonlinear tracking control using actor-critic reinforcement learning strategy”, IEEE Transactions on Industrial Informatics, vol. 15, no. 9, pp. 4969-4977, Sep. 2019, DOI: 10.1109/TII.2019.2894282.

Guoxing Wen*, Shuzhi Sam Ge, C. L. Philip Chen, Fangwen Tu, Shengnan Wang, “AdaptiveTracking Control of Surface Vessel Using Optimized Backstepping Technique”, IEEE Transactions on Cybernetics, vol. 49, no. 9, pp. 3420 - 3431, Sep. 2019, DOI: 10.1109/TCYB.2018.2844177.

Guoxing Wen*, C. L. Philip Chen, Jun. Feng and Ning. Zhou, "Optimized Multi-Agent Formation Control Based on Identifier-Actor-Critic Reinforcement Learning Algorithm", in IEEE Transactions on Fuzzy Systems, vol. 26, no. 5, pp.2719 - 2731, Oct. 2018, DOI: 10.1109/TFUZZ.2017.2787561.

Guoxing Wen*, Shuzhi Sam Ge, Fangwen Tu, "Optimized Backstepping for Tracking Control of Strict Feedback Systems”, IEEE Transactions on Neural Networks and Learning Systems, vol. 28, no. 8, pp. 3850-3862, Aug. 2018, DOI: 10.1109/TNNLS.2018.2803726.

Guoxing Wen*, C. L. Philip Chen, Yan-Jun Liu, "Formation Control with Obstacle Avoidance for a class of Stochastic Multi-Agent Systems", IEEE Transactions on Industrial Electronics, vol. 65, no. 7, pp. 5847-5855, Jul. 2018, DOI: 10.1109/TIE.2017.2782229.

Guoxing Wen*, Shuzhi Sam Ge, Fangwen Tu, “Artificial Potential-Based Adaptive H∞ Synchronized Tracking Control for Accommodation Vessel”, IEEE Transactions on Industrial Electronics, vol. 64, no. 7, pp. 5640-5647, July 2017, DOI: 10.1109/TIE.2017.2677330.

Guoxing Wen^*, C. L. Philip Chen, “Optimized Backstepping Consensus Control Using Reinforcement Learning for a Class of Nonlinear Strict-Feedback-Dynamic Multi-Agent Systems”, IEEE Transactions on Neural Networks and Learning Systems, vol. , no. , pp. , DOI: 10.1109/TNNLS.2021.3105548.

Guoxing Wen^*, Liguang Xu, Bin Li, “Optimized Backstepping Tracking Control Using Reinforcement Learning for a Class of Stochastic Nonlinear Strict-Feedback Systems”, IEEE Transactions on Neural Networks and Learning Systems, vol. , no. , pp. , DOI: 10.1109/TNNLS.2021.3105176.

Guoxing Wen^*, Chenyang Zhang, Ping Hu, Yang Cui, "Adaptive Neural Network Leader-Follower Formation Control for a Class of Second-Order Nonlinear Multi-Agent Systems with Unknown Dynamics," in IEEE Access, vol. 8, pp. 148149-148156, Oct. 2020, DOI: 10.1109/ACCESS.2020.3015957.

Guoxing Wen^*, C. L. Philip Chen, Bin Li, “Optimized Formation Control Using SimplifiedReinforcement Learning for a Class ofMultiagent Systems with Unknown Dynamics”,IEEE Transactions on Industrial Electronics, vol. 67, no. 9, pp. 7879 – 7888, Sept. 2020, DOI:10.1109/TIE.2019.2946545.

Guoxing Wen^*, C. L. Philip Chen, Wei Nian Li, “Simplified optimized control using reinforcement learning algorithm for a class of stochastic nonlinear systems”, Information Sciences, vol. 517, pp. 230–243, May 2020, DOI: 10.1016/j.ins.2019.12.039.

Guoxing Wen^*, C. L. Philip Chen, Shuzhi Sam Ge, Hongli Yang, Xiaoguang Liu “Optimized adaptive nonlinear tracking control using actor-critic reinforcement learning strategy”, IEEE Transactions on Industrial Informatics, vol. 15, no. 9, pp. 4969-4977, Sep. 2019, DOI: 10.1109/TII.2019.2894282.

Guoxing Wen^*, Shuzhi Sam Ge, C. L. Philip Chen, Fangwen Tu, Shengnan Wang, “AdaptiveTracking Control of Surface Vessel Using Optimized Backstepping Technique”, IEEE Transactions on Cybernetics, vol. 49, no. 9, pp. 3420 - 3431, Sep. 2019, DOI: 10.1109/TCYB.2018.2844177.

Guoxing Wen^*, C. L. Philip Chen, Jun. Feng and Ning. Zhou, "Optimized Multi-Agent Formation Control Based on Identifier-Actor-Critic Reinforcement Learning Algorithm", in IEEE Transactions on Fuzzy Systems, vol. 26, no. 5, pp.2719 - 2731, Oct. 2018, DOI: 10.1109/TFUZZ.2017.2787561.

Guoxing Wen^*, Shuzhi Sam Ge, Fangwen Tu, "Optimized Backstepping for Tracking Control of Strict Feedback Systems”, IEEE Transactions on Neural Networks and Learning Systems, vol. 28, no. 8, pp. 3850-3862, Aug. 2018, DOI: 10.1109/TNNLS.2018.2803726.

Guoxing Wen^*, C. L. Philip Chen, Yan-Jun Liu, "Formation Control with Obstacle Avoidance for a class of Stochastic Multi-Agent Systems", IEEE Transactions on Industrial Electronics, vol. 65, no. 7, pp. 5847-5855, Jul. 2018, DOI: 10.1109/TIE.2017.2782229.

Guoxing Wen^*, Shuzhi Sam Ge, Fangwen Tu, “Artificial Potential-Based Adaptive H∞ Synchronized Tracking Control for Accommodation Vessel”, IEEE Transactions on Industrial Electronics, vol. 64, no. 7, pp. 5640-5647, July 2017, DOI: 10.1109/TIE.2017.2677330.