Additional information
by Bor-Sen Chen (Author)
This book introduces Multi-objective design methods to solve Multi-objective optimization problems of linear/non-linear dynamic systems under intrinsic random fluctuation and external disturbance. All proposed design methods are based on rigorous theoretical results and their applications are focused on practical engineering design example.
Author Biography
Bor-Sen Chen received a B.S. in electrical engineering from the Tatung Institute of Technology, Taipei, Taiwan, in 1970; an M.S. in geophysics from the National Central University, Chungli, Taiwan, in 1973; and a Ph.D. from the University of Southern California, Los Angeles, California, USA, in 1973. From 1973 to 1987, he was a lecturer, associate professor, and professor at the Tatung Institute of Technology. From 1987, he has been a professor, chair professor, and Tsing Hua distinguished chair professor with the Department of Electrical Engineering at National Tsing Hua University, Hsinchu, Taiwan. His research interests include robust control theory and engineering design, robust signal processing and communication system design, and systems biology. He has published more than 340 journal papers, including 150 papers in control, 80 papers in signal processing and communication, and 110 papers in systems biology. He has also published 10 monographs. He has been the recipient of numerous awards for his academic accomplishments in robust control, fuzzy control, H∞ control, stochastic control, signal processing, and systems biology, including four Outstanding Research Awards of the National Science Council, the Academic Award in Engineering from the Ministry of Education, National Chair Professor of the Ministry of Education, and Best Impact Award of IEEE Taiwan Section for having the most SCI citations of IEEE members in Taiwan. His current research interest focuses on the H∞ team formation network tracking control of large-scale unmanned aerial vehicles, large-scale biped robots and their team cooperation, deep neural network (DNN)-based control design of nonlinear dynamic systems, systems medicine design based on design specifications, and deep learning schemes. He is a life fellow of IEEE.
