Wentao Tang was born in Hunan Province, P.R. China. He received his B.S. in chemical engineering and a secondary degree in mathematics and applied mathematics from Tsinghua University in 2015, and his Ph.D. in chemical engineering at University of Minnesota in 2020. He was a process control engineer at Shell Global Solutions (U.S.) Inc., where he undertook multiple research projects for the development of Shell’s advanced process control software, prior to joining NC State University.
His current research focuses on developing data-driven control algorithms that integrate nonlinear control theory with machine learning techniques, which avoid detailed dynamic modeling procedures and can be more flexible for systems with complex dynamics. He is also interested in derivative-free algorithms for optimization problems without explicit algebraic models, especially in how the solution of large-scale problems can benefit from the identification of underlying network topology, decomposition of networks into constituent subsystems and adoption of acceleration schemes.
Tang, W., Allman, A., Pourkargar, D. B., & Daoutidis, P. (2018). Optimal decomposition for distributed optimization in nonlinear model predictive control through community detection. Computers & Chemical Engineering, 111, 43-54.
Tang, W., & Daoutidis, P. (2018). Distributed adaptive dynamic programming for data-driven optimal control. Systems & Control Letters, 120, 36-43.
Tang, W., & Daoutidis, P. (2021). Dissipativity learning control (DLC): Theoretical foundations of input–output data-driven model-free control. Systems & Control Letters, 147, 104831.
Tang, W., & Daoutidis, P. (2021). Coordinating distributed MPC efficiently on a plantwide scale: The Lyapunov envelope algorithm. Computers & Chemical Engineering, 155, 107532.
Tang, W., & Daoutidis, P. (2022). Fast and stable nonconvex constrained distributed optimization: The ELLADA algorithm. Optimization and Engineering, 23, 259-301.