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2018.7—至今: 工作于 中国计量大学 机电工程学院 副教授

2013.9-2017.10: 就读于985院校东北大学攻读博士学位, 控制理论与控制工程专业

主要从事最优控制理论研究和机器人系统建模与仿真的研究

在SCI期刊IEEE Transactions on Neural Networks and Learning Systems(1区影响因子6.108),

Neurocomputing(影响因子3.317), International Journal of Adaptive Control and Signal Processing(影响因子1.346),

International Journal of System Science(影响因子2.285), 《控制理论与应用》, 《数学的实践与认识》等杂志上发表SCI等论文15余篇.

并受邀担任International Journal of Adaptive Control and Signal Processing审稿人.

所在团队为 智能机器人与计量检测研究团队,团队负责人 王斌锐副校长

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","tutorColumnMessageVoList":[{"tutorColumnMessageId":"18A0103159_02","tutorColumnCode":"02","tutorId":"18A0103159","columnMessage":"

1、主持 国家自然科学基金,项目批准号:61903351, 项目名称:饱和多控制器系统离策强化学习的有限时间最优控制,直接费用:23.00万元, 项目起止年月:2020年01月至 2022年 12月.

2、参与 国家重点研发计划项目子任务,项目号:2018YFB2101004,地下基础设施多感官机器人智能巡检技术,2018.09-2022.09.

3、主持 中国计量大学科研启动费1项.

4、主持 2020校级研究生课程建设—教材建设(机器人系统建模与仿真).

5、主持 浙江省自然科学基金:基于无模型ADP的气动肌肉驱动关节轨迹的最优跟踪控制(LY22F030009),经费10万,2022.1-2024.12.

6、主持 青年科技人才培育专项-气动肌肉驱动机器人的无模型饱和主从分层控制(2022YW20),经费10万,2022.1-2023.12.

7、主持 新工科背景下机器人系统建模与仿真课程教学研究与探索(KT2022123)项目.

8、主持 浙江省普通本科高校“十四五”教学改革项目,项目号:jg20220265 2023.1-2024.12.

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指导2015年全国大学生数学建模竞赛并获得国家二等奖;

指导2022浙江省机器人竞赛 三等奖.

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1、主持 国家自然科学基金,项目批准号:61903351, 项目名称:饱和多控制器系统离策强化学习的有限时间最优控制,直接费用:23.00万元, 项目起止年月:2020年01月至 2022年 12月.

2、参与 国家重点研发计划项目子任务,项目号:2018YFB2101004,地下基础设施多感官机器人智能巡检技术,2018.09-2022.09.

3、主持 中国计量大学科研启动费1项.

4、主持 2020校级研究生课程建设—教材建设(机器人系统建模与仿真).

5、主持 浙江省自然科学基金:基于无模型ADP的气动肌肉驱动关节轨迹的最优跟踪控制(LY22F030009),经费10万,2022.1-2024.12.

6、主持 青年科技人才培育专项-气动肌肉驱动机器人的无模型饱和主从分层控制(2022YW20),经费10万,2022.1-2023.12.

7、主持 新工科背景下机器人系统建模与仿真课程教学研究与探索(KT2022123)项目.

8、主持 浙江省普通本科高校“十四五”教学改革项目,项目号:jg20220265 2023.1-2024.12.

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[24]Cui, XH (Cui, Xiaohong); Wang, BR (Wang, Binrui); Wang, LN (Wang, Lina).Mixed H2 / H ∞ optimal hierarchical tracking control for partially unknown dynamics.Transactions of the institute of measurement and control,2025,47(4)700-712.

[23] Xiaohong Cui,Binbin Peng, Binrui Wang, Lina Wang. Event‐triggered neural experience replay learning for nonzero‐sum tracking games of unknown continuous‐time nonlinear systems, International Journal of Robust and Nonlinear Control, 2023, 33(12): 1-23.

[22] Xiaohong Cui#, Binrui Wang*, Lina Wang, Jiayu Chen. Online optimal learning algorithm for Stackelberg games with partially unknown dynamics and constrained inputs[J], Neurocomputing, 2021, 445: 1-11. (SCI, EI检索)

[21] Xiaohong Cui#, Binrui Wang*, Han Lu, Jiayu Chen. Design and passive training control of elbow rehabilitation robot[J]. Electronics, 2021, 10 (10): 1147-1164. (SCI, EI检索)

[20] Xiaohong Cui#*,Jiayu Chen, Binrui Wang; Su an Xu ; Off-policy algorithm based Hierarchical optimal control for completely unknown dynamic systems, Neurocomputing, 2022,488 669–680.(SCI, EI检索)

[19] Binrui Wang#, Xiaohong Cui*, Jianbo Sun and Yanfeng Gao, Parameters optimization of central pattern generators for hexapod robot based on multi-objective genetic algorithm,International Journal of Advanced Robotic Systems, 2021,18(5):1-13. (SCI, EI检索)

[18] Xiaohong Cui#, Huaguang Zhang*, Yanhong Luo and Peifu Zu. Online finite-horizon optimal learning algorithm for nonzero-sum games with partially unknown dynamics and constrained inputs[J]. Neurocomputing, 2016, 185: 37-44. (SCI, EI检索)

[17] Huaguang Zhang*, Xiaohong Cui, Yanhong Luo and He Jiang. Finite-Horizon tracking control for unknown nonlinear systems with saturating actuators[J]. IEEE Transactions on Neural Networks and Learning Systems, 29(4), 2018,1200-1212. (SCI, EI检索)

[16] Xiaohong Cui#, Huaguang Zhang*, Yanhong Luo and He Jiang. Finite-horizon optimal control of unknown nonlinear time-delay systems[J]. Neurocomputing, 2017, 238: 277-285. (SCI, EI检索)

[15] Xiaohong Cui#, Huaguang Zhang*, Yanhong Luo and He Jiang. Adaptive dynamic programming for tracking design of uncertain nonlinear systems with disturbances and input constraints[J]. International Journal of Adaptive Control and Signal Processing, 2017, 31(11): 1567-1583. (SCI, EI检索)

[14] Binrui Wang; Ke Zhang; Xuefeng Yang; Xiaohong Cui. The gait planning of hexapod robot based on CPG with feedback[J]. International Journal of Advanced Robotic Systems, 2020, 17 (3): 1-12. (SCI, EI检索)

[13] He Jiang, Huaguang Zhang, Yanhong Luo and Xiaohong Cui. H control with constrained input for completely unknown nonlinear systems using data-driven reinforcement learning method[J]. Neurocomputing, 2017, 237: 226-234. (SCI, EI检索)

[12] He Jiang, Huaguang Zhang, Kun Zhang and Xiaohong Cui. Data-driven adaptive dynamic programming schemes for non-zero-sum games of unknown discrete-time nonlinear systems[J]. Neurocomputing, 2018, 275: 649-658. (SCI, EI检索)

[13] He Jiang, Huaguang Zhang, Geyang Xiao and Xiaohong Cui. Data-based approximate optimal control for nonzero-sum games of multi-player systems using adaptive dynamic programming[J]. Neurocomputing, 2018, 275: 192-199. (SCI, EI检索)

[10] Kezhen Han, Jian Feng, Xiaohong Cui. Fault-tolerant optimized tracking control for unknown discrete-time linear systems using a combined reinforcement learning and residual compensation methodology[J]. International Journal of System Science. 2017, 48(13): 2811-2825. (SCI, EI检索)

[9] 崔小红#, 罗艳红, 张化光*, 祖培福. 未知饱和控制系统有穷域最优控制[J]. 控制理论与应用, 2016, 33(5): 631-637. (EI, 一级)

[8] 崔小红#, 王缔. 基于近似动态规划方法的未知系统的最优跟踪控制[J]. 数学的实践与认识, 2015, 45(11): 266-274.(中文核心)

[7] 崔小红#, 王缔. 带有控制约束的不确定非线性系统的 控制[J]. 数学的实践与认识, 2016, 46(22): 215-223.(中文核心)

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[1] 崔小红#, 陈家裕. 基于离策强化学习的小车倒立摆系统的鲁棒最优控制方法[P]. 中国发明专利:CN202110219290.9, 2021-6-18.(已公开)

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欢迎自动化、数学、电气及相关专业学生报考

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