姓名： 王炳昌
修改个人资料
性别： 男
民族： 汉族
出生年月： 1983.02
学历： 博士
职称： 副教授
导师信息： 硕士生导师
职务：

党派： 中国共产党
电话：

传真：

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： 山东大学控制科学与工程学院
研究方向： 多智能体(机器人)协作、随机控制与分布式计算、深度学习与强化学习、机器学习与人工智能
通信地址： 济南市经十路17923号山东大学千佛山校区控制科学与工程学院

社会兼职及奖励
主要兼职: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)。
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—至今，山东大学控制科学与工程学院，副研究员；
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



科研项目：
1.山东大学青年****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, 在研, 独立.
2.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，2015/01-2017/12,63万，在研，主持.
3. 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，在研，主持.
4.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，在研，独立.
5.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.
6.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.
7.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.
8.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，在研，参与.
9. 山东大学自主创新基金，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：
欢迎对多智能体(机器人)协作、分布式计算与优化、随机控制与随机算法、机器学习与人工智能、智能电网与电力市场等方向感兴趣的学生报考(可与加拿大, 澳大利亚同方向的导师联合培养).
在国际期刊和会议上发表论文30余篇，主要包括:
期刊论文
[1] Bing-Chang Wang and Minyi Huang, Mean field production output control with sticky prices: Nash and social solutions, Automatica, 100, 90-98, 2019.
[2] 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.
[3] Yibing Sun, Minyue Fu, Bingchang Wang, Huanshui Zhang, Distributed dynamic state estimation with parameter identification for large-scale systems, Journal of The Franklin Institute, 354(14), 2017.
[4] 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. [5] 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.
[6] Yibing Sun，Minyue Fu，Bing-Chang Wang，Huanshui Zhang, 大规模动态系统的分布式状态估计算法，山东大学学报 (工学版)，2016，46(6): 62~68.
[7] Qiang Zhang, 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.
[8] 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.
[9] 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.
[10] Bing-Chang Wang*, and Ji-Feng Zhang, Distributed output feedback control of Markov jump multi-agent systems, Automatica, 49(5), 1397-1402, 2013.
[11] 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.
[12] 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)
[13] 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.
[14] 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.
[15] 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)
[16] Bing-Chang Wang, and Hai-Ling Dong, Some local asymptotic results on Markov renewal theorems, Mathematica Applicata, 2010, 23 (2), 237-243. (in Chinese)
[17] 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)
[18] 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)
[19] Bing-Chang Wang and Minyue Fu, Optimal sampling for state estimation of discrete-time linear systems, preprint.
[20] Bing-Chang Wang, and Zhiyun Lin, Stochastic sensor scheduling via a Markov switching approach, working paper.

会议论文
[1] 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.
[2] Bing-Chang Wang，and Minyi Huang, Mean field social optima in production output adjustment，Proceedings of the 35th Chinese Control Conference，Chengdu，July, 2016.
[3] 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.
[4] Yibing Sun, Minyue Fu, Bingchang Wang, Huanshui Zhang, A distributed MAP approach to dynamic state estimation with applications in power networks, Proceedings of 14^th European Control Conference, Linz, Austria, 2015.
[5] 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.
[6] 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.
[7] 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.
[8] 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.
[9] 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.
[10] 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.
[11] Bing-Chang Wang, and Ji-Feng Zhang, Distributed control of multi-agent systems with major agents and Markov parameters, Proceedings of the 30^th Chinese Control Conference, Yantai, China, July 22-24, 2011, pp. 4835-4840.
[12] 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