College of Science,
Shandong University of Aeronautics
Binzhou, Shandong, PR China, 256600
E-mail: wengx_bzu@hotmial.com
职称晋升
1. 2021.12-至今 教授山东航空学院理学院
2. 2019.12-2021.12 副教授滨州学院理学院
3. 2016.9-2019.12 讲师滨州学院理学院
出国（境）经历
1. 2015.09-2016.09 新加坡国立大学-工程学院博士后导师：葛树志
2. 2015.07-2015.12 澳门大学-科技学院访问学者导师：陈俊龙
3. 2018.06-2018.09 澳门大学-科技学院访问学者导师：陈俊龙
4. 2019.09-2019.12 澳门大学-科技学院访问学者导师：万锋
社会兼职
1.中国自动化学无人飞行器自主控制专业委员会委员 第二届 2024.05-2028.12
2.山东自动化学会理事会理事 第九届 2022.12-2027.12
3.曲阜师范大学-电气工程专业 兼职硕士研究生导师 2018.01 -2021.12
4.山东科技大学-自动化学院 兼职硕士研究生导师 2019.10
5.齐鲁工业大学-数学学院 兼职硕士研究生导师 2020.06-至今
荣誉称号
1.2024.06 授予称号：滨州市优秀共产党员 授予单位：中共滨州市委
2. 2020.05 授予称号：滨州市“五一劳动”奖章 授予单位：滨州市总工会
3. 2020.02-2025.01 授予称号：山东省具有突出贡献的中青年专家 授予单位：山东省人民政府
4.2019.06-2022.06 授予称号：校聘教授 授予单位：滨州学院
5.2018.03-2021.02 授予称号：“聚英计划” 第二层次 授予单位：滨州学院
6. 全球前2%科学家榜单：2021年度（2020年）第183152位、2022年度（2021年）第135263位，2023年度（2022年）第106635名，2024年度（2023年）第65843名
科研奖励
1. 文国兴（1/2）; 非线性系统的强化学习优化控制, 山东省自动化学会科学技术奖-自然科学奖，一等奖，授奖单位：山东省自动化学会，2024.02（文国兴，李彬）
2. 文国兴（1/2）; 基于自适应学习策略的非线性多智能体协同控制, 滨州市科学技术奖-自然科学奖，三等奖，授奖单位：滨州市人民政府，2022.12（文国兴，冯君）。
3.文国兴（1/3）; 严格反馈系统跟踪控制的Optimized Backstepping方法, 滨州市自然科学优秀学术成果奖，一等奖，授奖单位：滨州市科学技术协会，2021.03（文国兴，葛树志，屠方闻）。
4. 文国兴（1/2）; 非线性多智能体的一致控制，山东省高等学校科学技术奖，三等奖，授奖单位：山东省教育厅，2019.12 (文国兴，冯君)。
5. 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， 2019.06（文国兴）。
6. 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， 2018.05（文国兴，陈俊龙）。
7. 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, 滨州市自然科学优秀学术成果奖，一等奖，授奖单位：滨州市科学技术协会，2017.12（文国兴，陈俊龙，刘艳军）。
主持项目
1. 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：2021.01-2024.12
2. 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：团队带头人, 日期：2019.10-2021.12
3. 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：2018.03-2021.06
4. 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：2016.03-2019.02
指导学生项目：
1. 大学生创新创业训练计划项目：非线性多智能体的强化学习优化编队控制；类别：国家级，一般项目；项目编号：202410449011；项目期限：一年；指导教师：文国兴；项目负责人：李航/2214070120；成员：钟燕燕/2214070138，张文静/2214070201，金鹏/2014070104，崔伟豪/2014070109，张晓瑞
参与项目：
1. 山东省自然科学基金（面上项目）：基于线性自抗扰控制的自适应循环发动机全包线实时控制研究（No：ZR2023MF027 ），资助金额：10万元，2024.01-2026.12，位次：2/3 (谢振伟立项)
2. 山东省自然科学基金（面上项目）：传感器网络的动态事件触发分布式滤波问题研究（No：ZR2021MF088 ），资助金额：10万元，2022.01-2024.12，位次：3/6 (王少英立项)
3. 山东省自然科学基金（面上项目）：基于深度学习无人机对地运动目标跟踪算法研究（No：ZR2020MF142），资助金额：10万元，2021.01-2023.12，位次：7/7 (王海军立项)。
4. 国家级项目，国家自然科学基金（青年项目），网络化随机系统的事件触发滤波与故障检测，基金号：61703050，资助金额：25万，位次：4/6, 日期：2018.01-2020.12 （王少英立项）。
5. 省级项目，山东省自然科学基金（青年项目），一些与连分数有关的集合的packing维数，基金号：ZR2019QA003，资助金额：13万，位次：3/5, 日期：2019.07-2022.06 （麻连刚立项）。
学术论文：
通讯并首作论文
1. Guoxing Wen*, Ben Niu, Optimized distributed formation control using identifier-critic-actor reinforcement learning for a class of stochastic nonlinear multi-agent systems, in ISA Transactions, vol. , no. , pp. , 2024, doi: .
2. Guoxing Wen*, Dengxiu Yu, Yanlong Zhao, Optimized Fuzzy Attitude Control of Quadrotor Unmanned Aerial Vehicle Using Adaptive Reinforcement Learning Strategy, in IEEE Transactions on Aerospace and Electronic Systems, vol. 60, no. 5, pp. 6075-6083, Oct. 2024, doi: 10.1109/TAES.2024.3401668.
3. Guoxing Wen*, Ranran Zhou, Yanlong Zhao and Ben Niu, Optimized Backstepping Combined with Dynamic Surface Technique for Single-Input-Single-Output Nonlinear Strict Feedback System, in IEEE Transactions on Systems, Man, and Cybernetics-Systems, vol. 54, no. 7, pp. 4210-4221, July. 2024, doi: 10.1109/TSMC.2024.3379356.
4. Guoxing Wen*, Yongchao Liu, “Tracking control based on adaptive Bernstein polynomial approximation for a class of unknown nonlinear dynamic systems”, Journal of the Franklin Institute, vol. 360, no. 7, Pages 5082-5091, May 2023, DoI: 10.1016/j.jfranklin.2023.03.011.
5. 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. 34, no. 3, pp. 1524-1536, March 2023, doi: 10.1109/TNNLS.2021.3105548.
6. 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. 34, no. 3, pp. 1291-1303, March 2023, doi: 10.1109/TNNLS.2021.3105176.
7. Guoxing Wen*, Hui Dou, Bin Li, “Adaptive fuzzy leader-follower consensus control using sliding mode mechanism for a class of high-order unknown nonlinear dynamic multi-agent systems”, International Journal of Robust Nonlinear Control, 33(1): 545-558, Jan. 2023, doi:10.1002/rnc.6460
8. Guoxing Wen*, Bin Li, Ben Niu, Optimized Backstepping Control Using Reinforcement Learning of Observer-Critic-Actor Architecture Based on Fuzzy System for a Class of Nonlinear Strict-Feedback Systems, in IEEE Transactions on Fuzzy Systems, vol. 30, no. 10, pp. 4322-4335, Oct. 2022, doi: 10.1109/TFUZZ.2022.3148865.
9. Guoxing Wen*, and Bin Li, Optimized Leader-Follower Consensus Control Using Reinforcement Learning for a Class of Second-Order Nonlinear Multiagent Systems, in IEEE Transactions on Systems, Man, and Cybernetics-Systems, vol. 52, no. 9, pp. 5546-5555, Sept. 2022, doi: 10.1109/TSMC.2021.3130070.
10. Guoxing Wen*, Wei Hao, Weiwei Feng and Kaizhou Gao, Optimized Backstepping Tracking Control Using Reinforcement Learning for Quadrotor Unmanned Aerial Vehicle System, in IEEE Transactions on Systems, Man, and Cybernetics-Systems, vol. 52, no. 8, pp. 5004-5015, Aug. 2022, doi: 10.1109/TSMC.2021.3112688.
11. Guoxing Wen*, Ben Niu, “Optimized tracking control based on reinforcement learning for a class of high-order unknown nonlinear dynamic systems”, Information Sciences, vol. 606, pp. 368-379, Aug. 2022, DOI: https://doi.org/10.1016/j.ins.2022.05.048
12. 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（高被引论文）.
13. 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.
14. 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.
15. 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.
16. 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.
17. Guoxing Wen*, Shuzhi Sam Ge, C. L. Philip Chen, Fangwen Tu, Shengnan Wang, “Adaptive Tracking 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.
18. 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 Sciences, vol. 62, no. 9, pp. 192205:1-192205:14, July 2019, DOI:CNKI:SUN:JFXG.0.2019-09-011.
19. 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.
20. 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. 29, no. 8, pp. 3850-3862, Aug. 2018, DOI: 10.1109/TNNLS.2018.2803726.
21. Guoxing Wen*, C. L. Philip Chen, Yan-Jun Liu, Formation Control with Obstacle Avoidance for a class of Stochastic Multiagent Systems, IEEE Transactions on Industrial Electronics, vol. 65, no. 7, pp. 5847-5855, Jul. 2018, DOI: 10.1109/TIE.2017.2782229.
22. Guoxing Wen*, C. L. Philip Chen, Yan-Jun Liu, Zhi Liu, “Neural Network-Based Adaptive Leader-Following Consensus Control for a Class of Nonlinear Multiagent 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）.
23. 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.
24. 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）.
25. 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.
26. 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.
27. 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.
指导研究生论文
1． Wenxia Sun, Shuaihua Ma, Bin Li and Guoxing Wen*, “Optimized inverse dead-zone control using reinforcement learning for a class of nonlinear systems,” in International Journal of Adaptive Control and Signal Process, vol. , no. , pp. , . 2024, DOI: 10.1002/acs.3913
2． Wenxia Sun, Shuaihua Ma, Bin Li and Guoxing Wen*, Optimised backstepping control for the nonlinear strict-feedback system having unknown control dead-zone, in International Journal of Control, vol. , no. , pp. , . 2024, DOI: 10.1080/00207179.2024.2364357
3． Shuaihua Ma and Wenxia Sun and Bin Li and Guoxing Wen*, “Optimized dead-zone control based on sliding-mode mechanism for a class of unknown nonlineardynamic systems,” in Transaction of the Institute of Measurement and Control, , vol. , no. , pp. , . 2024, DOI:
4． Shuaihua Ma and Wenxia Sun and Guoxing Wen*, “Optimized dead-zone inverse control using reinforcement learning and sliding-mode mechanism for a class of high-order nonlinear systems,” in European Journal of Control, vol.80, part B, pp. 101132, Nov. 2024, DOI: https://doi.org/10.1016/j.ejcon.2024.101132
5． Zijun Li, Yanfen Song and Guoxing Wen*, Optimized leader-follower consensus control for high-order nonlinear multi-agent system modeled in canonical dynamic form, in Journal of the Franklin Institute, vol. 361, no. 7, pp. 106808, May. 2024, doi: org/10.1016/j.jfranklin.2024.106808..
6． Yanfen Song, Zijun Li, Guoxing Wen*, Optimized tracking control using reinforcement learning and backstepping technique for canonical nonlinear unknown dynamic system”, in Optimal Control Application Method, vol.45, no. 4, pp. 1655-1671, Feb. 2024, doi: 10.1002/oca.3115
7． Yanfen Song, Zijun Li, Bin Li and Guoxing Wen*, Optimized leader-follower consensus control using combination of reinforcement learning and sliding mode mechanism for multiple robot manipulator system, in International Journal of Robust and Nonlinear Control, vol. 34, no. 8, pp. 5212-5228, Feb. 2024, doi: 10.1002/rnc.7259.
8． Zijun Li, Yanfen Song and Guoxing Wen*, Reinforcement Learning Based Optimized Sliding-Mode Consensus Control of High-Order Nonlinear Canonical Dynamic Multiagent System, in IEEE Systems Journal, vol. 17, no. 4, pp. 6302-6311, Dec. 2023, doi: 10.1109/JSYST.2023.3280192.
9． Ranran Zhou, Guoxing Wen*, Jiahao Zhu, Bin Li, “Adaptive neural network observer control for a class of nonlinear strict feedback systems with unmeasurable states”, in International Journal of Control, vol. 97, no. 2, pp. 165-174, Feb. 2024, DOI:10.1080/00207179.2022.2136109
10．Ranran Zhou, Guoxing Wen*, Bin Li, “Reinforcement Learning-Based Optimized Backstepping Control of Nonlinear Strict Feedback System with Unknown Control Gain Function,” in Optimal Control Applications and Methods, Vol. 43, No. 5, pp.1358-1378, Sep. 2022, DOI: 10.1002/oca.2895
11．Jiahao Zhu, Guoxing Wen*, Bin Li, “Decentralized adaptive formation control based on sliding mode strategy for a class of second-order nonlinear unknown dynamic multi-agent systems,” in International Journal of Adaptive Control and Signal Process, Vol. 36, No. 4, pp.1045-1058, Apr. 2022, doi:10.1002/acs.3381.
12．Bin Li, Jiahao Zhu, RanRan Zhou, Guoxing Wen*, “Adaptive Neural Network Sliding Mode Control for a Class of SISO Nonlinear Systems”. Mathematics. Vol.10, No. 7, pp.1182, Apr.2022, DOI: 10.3390/math10071182
从作论文
1． Jiang W, Wen G, Ge SS, “Adaptive switching control of full state constrained nonlinear systems with unknown control directions,” International Journal of Robust Nonlinear Control, vol., No. , pp. , Apr. 2024, doi: 10.1002/rnc.7634
2． Jiahao Zhu, Guoxing Wen, Kalyana C. Veluvolu, “Optimized backstepping consensus control using adaptive observer-critic–actor reinforcement learning for strict-feedback multi-agent systems,” Journal of the Franklin Institute, vol. 361, no. 6, pp. 106693, 2024, doi: https://doi.org/10.1016/j.jfranklin.2024.106693
3． Jia Long, Dengxiu. Yu, Guoxing. Wen, Li Li, Zhen Wang and C. L. Philip Chen, Game-Based Backstepping Design for Strict-Feedback Nonlinear Multi-Agent Systems Based on Reinforcement Learning, in IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 1, pp. 817-830, Jan. 2024, doi: 10.1109/TNNLS.2022.3177461. (因未见刊，未期未登记)
4． Jie Lan, Yan-Jun Liu, Dengxiu Yu, Guoxing, Wen, Shaocheng. Tong and Lei Liu, Time-Varying Optimal Formation Control for Second-Order Multiagent Systems Based on Neural Network Observer and Reinforcement Learning, in IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 3, pp. 3144-3155, March 2024, doi: 10.1109/TNNLS.2022.3158085. (因未见刊，未期未登记)
5． Bin Li, Xue Yang, Ranran Zhou & Guoxing Wen (2022) Reinforcement learning-based optimized control for a class of second-order nonlinear dynamic systems, International Journal of Systems Science, 53:15, 3154-3164, DOI: 10.1080/00207721.2022.2074568
6． Xue Yang, Bin Li and Guoxing Wen, Adaptive Neural Network Optimized Control Using Reinforcement Learning of Critic-Actor Architecture for a Class of Non-Affine Nonlinear Systems, in IEEE Access, vol. 9, pp. 141758-141765, Oct. 2021, doi: 10.1109/ACCESS.2021.3120835.
7． Yongchao Liu, Qidan Zhu, Guoxing Wen*, Adaptive Tracking Control for Perturbed Strict-Feedback Nonlinear Systems Based on Optimized Backstepping Technique, in IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 2, pp. 853-865, Feb. 2022, doi: 10.1109/TNNLS.2020.3029587.
8． Su Hongwei, Zhang Zi-Wei , Wen Guoxing*, Yan, Guan. Analysis of epidemic propagation using mean field theory on signed graphs[J]. Modern Physics Letters B, Volume 36, Issue 6, id. 2150594-1370, Feb. 2022.
9． Yang Cui, Xiaoping Liu, Xin Deng, Guoxing Wen, Command-Filter-Based Adaptive Finite-Time Consensus Control for Nonlinear Strict-Feedback Multi-Agent Systems with Dynamic Leader, Information Sciences, Vol. 565, July 2021, pp.17-31, July 2021, DOI: 10.1016/j.ins.2021.02.078.
10．Qidan Zhu, Yongchao Liu, Guoxing Wen, “Adaptive neural network control for time-varying state constrained nonlinear stochastic systems with input saturation”, Information Sciences, vol. 527, pp. 191-209, July 2020, DOI: 10.1016/j.ins.2020.03.055.
11．Qidan Zhu, Yongchao Liu, Guoxing Wen, “Adaptive neural network output feedback control for stochastic nonlinear systems with full state constraints”, ISA Transactions, vol. 101, pp. 60-68, June 2020, DOI:10.1016/j.isatra.2020.01.021.
12．N. Zhou, R. Chen, L. Li and Guoxing Wen, “Design of Variable-Gain First Order Sliding Mode and its Application to Spacecraft Attitude Synchronization”, IEEE Access, vol. 7, pp. 137543-137551, Sep. 2019, DOI: 10.1109/ACCESS.2019.2943139.
13．Zhou Ning, Riqing Chen, Yuanqing Xia, Jie Huang, Guoxing Wen, “Neural network–based reconfiguration control for spacecraft formation in obstacle environments”, International Journal of Robust & Nonlinear Control, vol. 28, no.6, pp. 2442-2456, April. 2018, DOI: 10.1002/rnc.4025.
14．C. L. Philip Chen, Guo-Xing Wen, Yan-Jun Liu, Zhi Liu, “Observer-Based Adaptive Backstepping Consensus Tracking Control for High-Order Nonlinear Semi-Strict-Feedback Multi-Agent Systems”, IEEE Transactions on Cybernetics, vol. 46, no. 7, pp. 1591-1601, July 2016, DOI: 10.1109/TCYB.2015.2452217 （高被引论文）.
15．Jun Feng, Guo-Xing Wen*, “Adaptive NN consensus tracking control of a class of nonlinear multi-agent systems”, Neurocomputing, vol. 151, no. 1, pp. 288-295, Mar. 2015, DOI: 10.1016/j.neucom.2014.09.037.
16．C. L. Philip Chen, Guo-Xing Wen, Yan-Jun Liu, and Fei-Yue Wang, “Adaptive Consensus Control for a Class of Nonlinear Multi-agent Time-Delay Systems Using Neural Networks”, IEEE Transaction on Neural network and learning systems, vol. 25, no. 6, pp. 1217-1226, Jun. 2014, DOI: 10.1109/TNNLS.2014.2302477（高被引论文）.
17．C. L. Philip Chen, Yan-Jun Liu, and Guo-Xing Wen, “Fuzzy Neural Network-Based Adaptive Control for a Class of Uncertain Stochastic Systems”, IEEE Transaction on Cybernetics, vol. 44, no. 5, pp. 583 – 593, May 2014, DOI: 10.1109/TCYB.2013.2262935（高被引论文）.
18．Yan-Jun Liu, C. L. Philip Chen, Guo-Xing Wen, and Shaocheng Tong, Adaptive Neural Output Feedback Tracking Control for a Class of Uncertain Discrete-Time Nonlinear Systems, in IEEE Transactions on Neural Networks, vol. 22, no. 7, pp. 1162-1167, July 2011, doi: 10.1109/TNN.2011.2146788.（高被引论文）.
19．Yan-Jun Liu, Guo-Xing Wen, Shao-Cheng Tong, Direct adaptive NN control for a class of discrete-time nonlinear strict-feedback systems, Neurocomputing, Vol. 73, No.13-15, pp. 2498–2505, Aug. 2010, DOI: 10.1016/j.neucom.2010.06.001.
会议论文
1. Guo-Xing Wen, C.L. Philip Chen, Ning Zhou4, Zhao Xu，“Neural Network Based Adaptive Backstepping Tracking Controlfor Accommodation Vessel,” the 36th Chinese Control Conference (CCC 2017) 26-28 July 2017, Dalian, China, pp. 3453-3457.
2. Guo-Xing Wen, C.L. Philip Chen, “Robust Adaptive Leader-Following Consensus Control for a Class of Nonlinear Multi-agent Systems,” 2013 Chinese Automation Congress (CAC 2013) 7-8 Nov 2013, Changsha, China
3. Guo-Xing Wen, C.L. Philip Chen, “Adaptive neural leader-following consensus control for a class of nonlinear multi-agent systems,” 2013 CACS International Automatic Control conference 2-4 Dec. 2013, Nantou, TaiWan, UK, pp: 221 – 226 (Best student paper nomination).
4. Guo-Xing Wen, C.L. Philip Chen, “Adaptive NN Consensus Control for a Class of Nonlinear Multi-agent Time-Delay Systems,” 2013IEEE International Conference on Systems, Man, and Cybernetics 13-16 Oct. 2013, COEX, Manchester, UK, pp: 4941 – 4946
5. Guo-Xing Wen, C.L. Philip Chen, “Distributed Consensus Control Using Neural Network for a Class of Nonlinear Multi-agent Systems” 2012 IEEE International Conference on Systems, Man, and Cybernetics October 14-17, 2012, COEX, Seoul, Korea, pp:2591-1595
6. Guo-Xing Wen, Yan-Jun Liu, Adaptive Robust NN Control of Nonlinear Systems, ISNN2011. pp. 535-541.
教学论文
1. 文国兴,窦慧.浅谈科研融入高校教学的意义[J].教育教学论坛, 2018(40):2，DOI:CNKI:SUN:JYJU.0.2018-40-102.
教研项目
1. 校级项目，面向航空类专业的高等数学教学研究与实践，BZXYYB201732，0.3万，5/5，2017.12-2020.12
2. 山东航空学院教学研究项目, 名称：“导向需求，学科交叉，产学结合”—人工智能背景下无人机类本科专业建设研究与实践，类型：一般项目，编号：SHYJYYB202322，2024.01 -2025.12
3. 山东航空学院研究生教育教学改革研究项目, 名称：学科交叉，融合发展--面向航空智能控制的创新型专硕人才培养模式探索与实践，SHYJG202306，2024.01-2025.12，1/9
专利
1. 发明人：文国兴, 宋燕芬, 李彬, 刘丽霞, 利权人：齐鲁工业大学（山东省科学院），名称：一种高阶多智能体的强化学习优化控制器构建方法及系统，类型：发明专利，专利号：ZL 2023 1 0446816.6，专申请日：2023.04.19，公告日：2023.11.14, 授权公告号：CN 116500893 B
2. 发明人：文国兴，专利权人：滨州学院，名称：一种机械自动化抓取装置，类型：发明专利，专利号：ZL 2021 1 0337414. 3，申请日：2021.03.30，公告日：2022.11. 8, 授权公告号：CN 113043314 B
3. 发明人：文国兴，专利权人：滨州学院，名称：一种用于采摘棉花的植保无人机，类型：实用新型，专利号：ZL 2020 2 0347803. 5，申请日：2020.3.19，公告日：2020.11. 3, 授权公告号：CN 211831964 U
4. 发明人：文国兴，专利权人：滨州学院，名称：一种多孔数控钻孔机，类型：实用新型，专利号：ZL 2020 2 0347801. 6，申请日：2020.3.19，公告日：2020.10. 23，授权公告号：CN 211727572 U
5. 发明人：文国兴，专利权人：滨州学院，名称：一种监测运输无人机，类型：实用新型，专利号：ZL 2020 2 0347578. 5，申请日：2020.3.19，公告日：2020.10. 23, 授权公告号：CN 211731826 U
6. 发明人：文国兴, 利权人：滨州学院，名称：一种便携式电缆剥皮器，类型：实用新型，专利号：ZL 2020 2 0347786.5，专申请日：2020.3.19，公告日：2020.8.25, 授权公告号：CN 211351528 U
其他
1.杰出审稿人（Automatica期刊），2018年3月
2.第十二届全国大学生数学竞赛，二等奖，证书编号：CMS(鲁)F20201055, 中国数学会，获奖学生：周志亮，2020.12
3.第十二届全国大学生数学竞赛，二等奖，证书编号：CMS(鲁)F20201191, 中国数学会，获奖学生：周樊汝凤，2020.12，
