Wang Kang

 
Personal profile

Name: Wang Kang

Gender: Male

Degrees: Ph.D.

Title: Lecturer

E-mail: wangkang@bjut.edu.cn

 

Research Areas

1. Adaptive dynamic programming;

 

2. Multiple model adaptive control;

 

3. Modelling, optimization and control of complex system;

 

4. Artificial intelligence and its application in food safety.

 

Publications

 

1. Li X, Wang K, Jia C. Data-Driven Control of Ground-Granulated Blast-Furnace Slag Production Based on IOEM-ELM[J]. IEEE Access, 2019, 7: 60650-60660.

2. Kang Wang, Xiaoli Li, Iterative ADP Optimal Control for GGBS Production Based on Dynamic Target Optimization[J], IEEE ACCESS, 2019, 7: 132851-132858.

3. Kang Wang, Xiaoli Li, Chao Jia, Shengxiang Yang, Miqing Li, Yang Li. Multiobjective optimization of the production process for ground granulated blast furnace slags[J]. Soft Computing2018, 22:81778186.

4. Wang Kang, Li Xiao-Li, Li Yang. Multiple Model Adaptive Tracking Control Based on Adaptive Dynamic Programming[J]. Discrete Dynamics in Nature and Society, 2016, 2016: 1-12.

5. Li Xiaoli, Wang Kang, Yu Xiuming, Su Wei. CPS-based Multiple Model Adaptive Control of GGBS Production Process[J]. Acta Automatica Sinica, 2019, 45(07): 1354-1365.

6. Wang Kang, Li Xiaoli, Jia Chao, Song Guizhi. Optimal tracking control for slag grinding process based on adaptive dynamic[J]. Acta Automatica Sinica, 2016, 42(10): 1542-1551.

Personal Statement

Wang Kang is Lecturer and Graduate Student Supervisor with the Faculty of Information Technology, Beijing University of Technology. He received his B.E. and Ph.D. degrees in Control Science and Engineering from the School Of Automation And Electrical Engineering, University of Science and Technology Beijing, in 2012 and 2018, respectively. He has presided over the Beijing natural science foundation, Postdoctoral Foundations of Beijing and Chaoyang district, at the same time, participated three National Natural Science Foundations of China and two National Key Research And Development projects. His research interests include artificial neural networks, optimal control, and intelligent control.