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    教授 学院首页 > 师资队伍 > 教授 > 正文
    王炳昌

    作者：   时间：2018-12-17   点击数：
    姓名： 王炳昌 undefined
                                               
    性别： 男
    民族： 汉族
    出生年月： 1983.02
    学历： 博士
    职称： 教授
    导师信息： 硕士生导师，博士生导师
    职务：
    党派： 中国共产党
    电话：
    学科一： 控制科学与工程
    学科二：
    邮箱： bcwang@sdu.edu.cn
    个人主页：

    http://faculty.sdu.edu.cn/wangbingchang/zh_CN/lwcg/625430/list/index.htm
    所在院系： 澳门新葡平台网址8883官网
    研究方向：

    随机控制与分布式博弈、深度学习与强化学习、多智能体(机器人)协作、机器学习与人工智能
    通信地址： 济南市经十路17923号澳门新葡萄老版本8883千佛山校区澳门新葡平台网址8883官网

    社会兼职及奖励                                                                                                                                                                                                            

    IEEE Senior Member, 中国自动化学会青年工作委员会委员，中国自动化学会区块链专委会委员，中国自动化学会控制理论专业委员会随机系统学组、多自主体控制学组委员.

    2017年获第11届亚洲控制会议青年作者奖提名，2018年获IEEE CSS Beijing Chapter青年作者奖,  2019年关肇直奖提名(Final list 5篇之一)， 2021年获国家优秀青年基金，2022年获山东省杰出青年基金。

    担任Session Chair/Co-chair of Chinese Control Conference (CCC2014, 2016, 2017)，Program Committee Member of the 12th World Congress on Intelligent Control and Automation (WCICA 2016).

    担任IEEE Transactions on Automatic Control, SIAM Journal on Control and Optimization, Automatica, IEEE Transactions on Neural Networks and Learning Systems, Science China (Information Sciences), IEEE Conference on Decision and Control, IFAC World Congress 等国际期刊和会议的审稿人.        

    2011年7月, 在中国科学院系统科学所获得博士学位;

    2011年10月—2012年9月，阿尔伯塔大学(加拿大)，博士后;

    2012年9月—2013年9月，纽卡斯尔大学(澳大利亚)，Research Academic;

    2013年10月—2021年12月，澳门新葡平台网址8883官网，副研究员，副教授

    2014年11月—2015年5月，访问卡尔顿大学(加拿大)，Research Associate；

    2016年11月—2017年1月，访问香港理工大学，Research Associate；

    2017年3月，访问香港理工大学，Visiting Professor

    2018年 5月，访问香港理工大学，Visiting Professor

    2022年1月—至今，澳门新葡萄老版本8883杰出中青年学者，澳门新葡平台网址8883官网，教授

    2023年 4月，访问香港理工大学，Visiting Professor

    科研项目：                   

    1. 山东省自然科学基金杰出青年基金，随机不确定平均场博弈与控制，2023/01- 2025/12, 100万，在研，主持.

    2. 国家自然科学基金优秀青年基金，随机系统平均场博弈与控制，2022/01- 2024/12, 200万，在研，主持.

    3. 国家自然科学基金重大项目，含氢多能源供需系统协同运行的基础理论与关键技术，2022/01-2026/12, 1500万，在研，参与.

    4. 澳门新葡萄老版本8883青年学者未来计划。具有模型不确定性的平均场博弈及应用研究, 2018 /07-2023/ 06, 50万, 结题, 独立.

    4. 国家自然科学基金面上项目，具有模型不确定性和公共噪声的平均场博弈与控制及应用研究，2018/01-2021/12, 63万，结题，主持.

    5. 国家自然科学基金青年项目，事件驱动采样下随机系统的控制与估计及在传感器网络中的应用，2015/01-2017/12, 25万，结题，主持.

    6. 教育部留学回国人员基金，事件触发采样下随机系统的控制与估计，2015/01-2017 /12, 3万，结题，独立.

    7. 国家自然科学基金国际合作与交流项目,  基于复杂时空网络的分布式协同估计,2012/01-2016/12, 260万元, 结题, 参与.

    8. 国家自然科学基金面上项目，集值输出系统的随机辨识与适应控制，20 12/01-2015/12，59万元，结题, 参与.

    9. 国家自然科学基金面上项目, 代谢网络的模块化建模与控制理论，2012 /01-2015/12，42万元，结题, 参与.

    10. 国家自然科学基金青年项目，基于网络拓扑结构的随机多自主体系统分布式控制，2015/01-2017/12, 24万，结题，参与.

    11. 澳门新葡萄老版本8883自主创新基金，基于事件的控制与估计，2014/01-2016/12, 15万元，结题, 独立.

    招收研究生情况：                               

    欢迎对机器学习与随机算法、平均场博弈与控制、强化学习与人工智能、多智能体(机器人)协作、分布式计算与优化、智能电网与电力市场等方向感兴趣的学生报考(可与澳大利亚、香港同方向的导师联合培养).

    在国际期刊和会议上发表论文50余篇，主要包括:             

    期刊论文

    [1] Bingchang Wang, Huanshui Zhang, Minyue Fu*, and Yong Liang, Decentralized strategies for finite population linear–quadratic–Gaussian games and teams, Automatica (Regular Paper), 2023, 148, 110789.

    [2] Ma Xiao, Bing-Chang Wang, Huanshui Zhang*, Social optima in linear quadratic mean-field output feedback control, International Journal of Control, Automation and Systems, 21, 2476–2486, 2023.

    [3] Yong Liang, Bing-Chang Wang*, and Huanshui Zhang, Robust mean field linear quadratic social control: Open-loop and closed-loop strategies, SIAM Journal on Control and Optimization, 2022, 60(4):2184-2213.

    [4] Bingchang Wang, Huanshui Zhang，and Ji-Feng Zhang*, Linear quadratic mean field social control with common noise: a directly decoupling method, Automatica (Regular Paper), 2022,146, 110619.

    [5] Wenjun Zhang, Bing-Chang Wang* and Yong Liang, Differentially private consensus for second-order multi-agent systems with quantized communication, IEEE Transactions on Neural Networks and Learning Systems, 2022, DOI: 10.1109/TNNLS.2022.3207470.

    [6] Bingchang Wang*, Leader-follower mean field LQ games: A direct method, Asian Journal of Control, 2022, https://doi.org/10.1002/asjc.3007.

    [7] Bingchang Wang*, and Chao Wang, Periodic and event-based impulse control for linear stochastic systems with multiplicative noise, Asian Journal of Control, 2022, https://doi.org/10.1002/asjc.3040.

    [8] Ma Xiao, Bing-Chang Wang*, Huanshui Zhang, Discrete-time indefinite mean field linear quadratic games with multiplicative noise, Asian Journal of Control, 2022, https://doi.org/10.1002/asjc.3026.

    [9] Chao Wang, and Bingchang Wang*, Mean-Square Stabilization of Networked Sampled-Data Systems with Packet Losses: Critical Sampling Intervals, Journal of Systems Science and Complexity, 2022, 35, 1278–1292.

    [10] Bing-Chang Wang*, Huanshui Zhang, Indefinite linear quadratic mean field social control problems with multiplicative noise, IEEE Transactions on Automatic Control (Full Paper), 5221-5236, 66(11), 2021.

    [11] Bing-Chang Wang, Jianhui Huang, and Ji-Feng Zhang*, Social optima in robust mean field LQG control: From finite to infinite horizon, IEEE Transactions on Automatic Control (Full Paper), 1529-1544, 66(4), 2021.

    [12] Jianhui Huang, Bing-Chang Wang*, and Jiongmin Yong, Social optima in mean field linear- quadratic-Gaussian control with volatility uncertainty, SIAM Journal on Control and Optimization, 59(2): 825-856, 2021.

    [13] Tinghan Xie, Bing-Chang Wang*, and Jianhui Huang, Robust linear quadratic mean field social control: A direct approach, ESAIM: Control, Optimisation and Calculus of Variations,7(20), 2021.

    [14] Bing-Chang Wang, and Yong Liang*, Robust mean field social control problems with applications in analysis of opinion dynamics, International Journal of Control, https://doi.org/10.1080/00207179.2021.1971302.

    [15] Bing-Chang Wang*, Huanshui Zhang, and Ji-Feng Zhang, Mean field linear-quadratic control: Uniform stabilization and social optimality, Automatica (Regular Paper), 2020, 121, 109088.

    [16] Tianxiang Wang, Bing-Chang Wang*, and Yong Liang, Multi-agent graphical games with input constraints: an online learning solution, Control Theory and Technology, 18, 148-159, 2020.

    [17] Bing-Chang Wang*, Xin Yu, and Hailing Dong, Social optima in linear quadratic mean field control with unmodeled dynamics and multiplicative noise, Asian Journal of Control, 2020, 23, 1572-1582.

    [18] Bing-Chang Wang*, Xin Yu, and Dandan Pang, Algorithm implementation for distributed convex intersection computation, J. System Science and Complexity, 2020, 33, 15–25.

    [19] Bing-Chang Wang* and Minyi Huang, Mean field production output control with sticky prices: Nash and social solutions, Automatica, 100, 90-98, 2019.

    [20] Bing-Chang Wang, Yuan Hua Ni*, and Huanshui Zhang, Mean-field games for multiagent systems with multiplicative noises, International Journal of Robust and Nonlinear Control, 2019, 29(17): 6081-6104.

    [21] Hai-Ling Dong, Jia-Mu Zhou, and Bing-Chang Wang, Synchronization of nonlinearly and stochastically coupled Markovian switching networks via event-triggered sampling, IEEE Transactions on Neural Networks and Learning Systems, 29(11), 5691-5700, 2018.

    [22] Yibing Sun, Minyue Fu, Bingchang Wang, and Huanshui Zhang, Distributed dynamic state estimation with parameter identification for large-scale systems, Journal of The Franklin Institute, 354(14), 2017.

    [23] Bing-Chang Wang, and Ji-Feng Zhang, Social optima in mean field linear-quadratic-Gaussian models with Markov jump parameters, SIAM Journal on Control and Optimization, 55(1), 429-456, 2017.

    [24] Yibing Sun, Minyue Fu, Bingchang Wang, Huanshui Zhang, and Damian Marelli, Dynamic state estimation for power networks using distributed MAP technique, Automatica, 77(11), 27-37, 2016.

    [25] Yibing Sun，Minyue Fu，Bing-Chang Wang，and Huanshui Zhang, 大规模动态系统的分布式状态估计算法, 澳门新葡萄老版本8883学报(工学版), 46(6), 62-68, 2016.

    [26] Qiang Zhang, and Bing-Chang Wang*, and Ji-Feng Zhang, Distributed dynamic consensus under quantized communication data, International Journal of Robust and Nonlinear Control, 25, 1704-1720, 2015.

    [27] Bing-Chang Wang*, Xiangyu Meng and Tongwen Chen, Event based pulse-modulated control of linear stochastic systems, IEEE Transactions on Automatic Control, 59(8), 2144-2150, 2014.

    [28] Bing-Chang Wang*, and Ji-Feng Zhang, Hierarchical mean field games for multi-agent systems with tracking-type costs: Distributed epsilon-Stackelberg equilibria, IEEE Transactions on Automatic Control, 59(8), 2241-2247, 2014.

    [29] Bing-Chang Wang*, and Ji-Feng Zhang, Distributed output feedback control of Markov jump multi-agent systems, Automatica, 49(5), 1397-1402, 2013.

    [30] Bing-Chang Wang, and Ji-Feng Zhang*, Mean field games for large-population multiagent systems with Markov jump parameters, SIAM Journal on Control and Optimization, 50(4), 2308-2334, 2012.

    [31] Bing-Chang Wang, and Ji-Feng Zhang*, Distributed control of large population multiagent systems with random parameters and a major agent, Automatica, 48 (9), 2093-2106, 2012. (Regular paper)

    [32] Bing-Chang Wang, and Yuan-Yuan Liu*, Local asymptotics of a Markov modulated random walk with heavy tailed increments, Acta Mathematica Sinica, English Series (SCI), 27(9), 1843-1854, 2011.

    [33] Zhen-Ting Hou, and Bing-Chang Wang*, Makov skeleton process approach to a class of partial differential-integral equation systems arising in operation research, International Journal of Innovative Computing, Information and Control(SCI), 7(12), 6799-6814, 2011.

    [34] Bing-Chang Wang, and Ji-Feng Zhang, Consensus conditions of multi-agent systems with unbalanced topology and stochastic disturbances, Journal of Systems Science and Mathematical Sciences, 29(10), 1353-1365, 2009. (in Chinese)

    [35] Bing-Chang Wang, and Hai-Ling Dong, Some local asymptotic results on Markov renewal theorems, Mathematica Applicata, 2010, 23(2), 237-243. (in Chinese)

    [36] Bing-Chang Wang, Hai-Ling Dong, and Xiu-Li Chen, An expression of local equivalent relation on Markov renewal measure, Mathematica Applicata, 2009, 22(3), 485-489. (in Chinese)

    [37] Hai-Ling Dong, Zhen-Ting Hou, and Bing-Chang Wang, A Class of Markov-modulated continuous infectious disease model, Journal of Biomathematics, 2008, 23(1), 79-84. (in Chinese)


    会议论文

    [1] Baoqiang Zhang, and Bing-Chang Wang, An online Q-Learning design for stochastic differential LQ game with completely unknown dynamics, 41st Chinese Control Conference (CCC), Hefei, China, July, 2022, pp. 2338-2343.

    [2] Tinghan Xie and Bing-Chang Wang, Social optima in linear quadratic mean field control with heterogeneous agents, 13th Asian Control Conference (ASCC), Jeju, Korea, May, 2022, pp. 1607-1612.

    [3] Tinghan Xie, Bing-Chang Wang, Mean field linear quadratic social problem with a major player: a direct approach, China Automation Congress, 2022.

    [4] Yong Liang, Bing-Chang Wang, Huanshui Zhang, Finite and infinite clusters mean field control problems via graphon theory, 2021 China Automation Congress, Beijing, October 2021.

    [5] Wenjun Zhang, Yong Liang and Bing-Chang Wang, Differentially private coordination of second-order multi-agent systems via dynamic encoding-decoding, 40th Chinese Control Conference, Shanghai, July, 2021.

    [6] Bing-Chang Wang, Huanshui Zhang, and Minyue Fu, Mean field LQ games with a finite number of agents, 16th International Conference on Control, Automation, Robotics and Vision, Shenzhen, December, 2020.

    [7] Chao Wang, Bing-Chang Wang, Comparison of periodic and event based impulse control for first order stochastic systems with multiplicative noise, 16th International Conference on Control and Automation, Singapore, October, 2020.

    [8] Tian-Xiang Wang, Yong Liang, Bing-Chang Wang, Actor-critic based graphical games for discrete-time linear systems with input constraints, 39th Chinese Control Conference, Shenyang, July 2020.

    [9] Bing-Chang Wang, Linear quadratic mean field social control with random coefficients and common noise, 38th Chinese Control Conference, Guangzhou, July, 2019.

    [10] Xiao Ma, Bing-Chang Wang, Huanshui Zhang, Discrete-time linear quadratic mean-field social control, 38th Chinese Control Conference, Guangzhou, July, 2019.

    [11] Chao Wang, Bing-Chang Wang, Critical sampling control for linear systems with communication packet loss, 38th Chinese Control Conference, Guangzhou, July, 2019.

    [12] Bing-Chang Wang, Huanshui Zhang, Indefinite linear quadratic mean field social control with multiplicative noise, 15th IEEE International Conference on Control and Automation, Edinburgh, UK, July, 2019.

    [13] Yong Liang, Bing-Chang Wang, Robust mean field social optimal control with application to opinion dynamics, 15th IEEE International Conference on Control and Automation, Edinburgh, UK, July, 2019.

    [14] Bing-Chang Wang, A complete solution to mean field linear quadratic control, 37th Chinese Control Conference, Wuhan, July, 2018.

    [15] Bing-Chang Wang, Linear quadratic mean field games: Open-loop solutions, 8th Annual IEEE International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, Tianjin, July, 2018.

    [16] Xin Yu, Bing-Chang Wang, Dandan Pang, Social optima in mean field LQ control with local disturbance, 14th International Conference on Control and Automation, Alaska, USA, June, 2018.

    [17] Bing-Chang Wang, Minyi Huang, Mean field games for production output adjustment with noisy sticky prices, 30th Chinese Control and Decision Conference, Shenyang, June, 2018.

    [18] Xin Yu, Bing-Chang Wang, Hailing Dong, A distributed algorithm based on KKT conditions for convex intersection computation, 2017 Chinese Automation Congress, Jinan, October, 2017.

    [19] Zhipeng Li, Bing-Chang Wang, Huanshui Zhang, Minyue Fu, Qianqian Cai, Maximum principle for Mckean-Vlasov type semi-linear stochastic evolution equations, 2017 Chinese Automation Congress, Jinan, October, 2017.

    [20] Bing-Chang Wang, Jianhui Huang, Social optima in robust mean field LQG control, 11th Asian Control Conference, Gold Coast, Australia, December, 2017.

    [21] Bing-Chang Wang, Yuan-Hua Ni, and Dan-dan Pang, Mean field games for multi-agent systems with multiplicative noises, Proceedings of the 36th Chinese Control Conference，Dalian，July, 2017.

    [22] Bing-Chang Wang，and Minyi Huang, Mean field social optima in production output adjustment，Proceedings of the 35th Chinese Control Conference，Chengdu，July, 2016.

    [23] Bing-Chang Wang, and Minyi Huang, Dynamic production output adjustment with sticky prices: A mean field game approach, Proceedings of 54th IEEE Conference on Decision and Control(CDC), Osaka, Japan, 2015.

    [24] Yibing Sun, Minyue Fu, Bingchang Wang, Huanshui Zhang, A distributed MAP approach to dynamic state estimation with applications in power networks, Proceedings of 14th European Control Conference, Linz, Austria, 2015.

    [25] Yibing Sun, Minyue Fu, Bingchang Wang, Huanshui Zhang, Dynamic State Estimation in Power Systems Using A Distributed MAP Method. Proceedings of the 34th Chinese Control Conference, July 28-30, 2015, Hangzhou, 47-52.

    [26] Bing-Chang Wang, Mean field team decision problems for Makov jump multiagent systems, Proceedings of the 34th Chinese Control Conference, July 28-30, 2015, Hangzhou, 1845-1860.  

    [27] Bing-Chang Wang, and Minyue Fu, Comparison of periodic and event based sampling for linear state estimation, World Congress of the International Federation of Automatic Control (IFAC), Cape Town, South Africa, 2014.

    [28] Bing-Chang Wang, Mean field games for Markov jump multi-agent system，Proceedings of the 33th Chinese Control Conference, July 28-30, Nanjing, 2014, pp. 5397-5402.

    [29] Xiangyu Meng, Bingchang Wang, Tongwen Chen and Mohamed Darouach, Sensing and actuation strategies for event triggered stochastic optimal control. Proceedings of 52nd IEEE Conference on Decision and Control (CDC), Florence, Italy, 2013, pp. 3097-3102.

    [30] Bing-Chang Wang, and Ji-Feng Zhang, Stackelberg games of large population multiagent systems: Centralized and distributed strategies, Proceedings of the 31th Chinese Control Conference, Hefei, 2012, pp. 6303-6308.

    [31] Bing-Chang Wang, and Ji-Feng Zhang, Distributed control of multi-agent systems with major agents and Markov parameters, Proceedings of the 30th Chinese Control Conference, Yantai, China, July 22-24, 2011, pp. 4835-4840.

    [32] Bing-Chang Wang, and Ji-Feng Zhang, Mean field games for large-population stochastic multi-agent systems with Markov jump parameters，Proceedings of the 29th Chinese Control Conference, July 29-31, Beijing, 2010, pp. 4572-4577.


    会议报告           

    [1] 具有乘机噪声多智能体系统的平均场博弈，第一届系统科学会议，邀请报告，北京，2017.

    [2] 具有粘性价格的动态产量调节：平均场博弈方法, 第14届中国工业与应用数学学会学术年会，湘潭，2016.

    [3] Dynamic production output adjustment with sticky prices: A mean field game approach, 54th IEEE Conference on Decision and Control(CDC), 大阪, 日本, 2015.        

    [4] Team decision problem for Markov jump mean field models, 第8届国际工业与应用数学会议，北京, 2015.

    [5] 带有马尔科夫跳变参数的平均场博弈, 侯振挺教授诞辰80周年及Markov过程相关领域研讨会, 中南大学, 长沙, 2015.

    [6] Comparison of periodic and event based sampling for linear state estimation, IFAC世界大会, 开普敦, 南非, 2014.

    [7] 事件驱动的控制、估计和调度, 第十届复杂系统与网络科学，东南大学，南京, 2014.      

    主讲课程： 随机过程，运筹学                              

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