Abstract: 

The multiple model approach provides a powerful tool for identification and control of nonlinear systems. Among different multiple model structures, the piecewise affine (PWA) models have drawn most of the attention in the past two decades. However, there are two major issues for the PWA model based identification and control: the curse of dimensionality and the computational complexity. To resolve these two issues, we propose a novel multiple model approach in this paper. Different from PWA models in which all dimensions of the regressor space are engaged in the partitioning, the key idea of the proposed multiple model architecture is to partition only the range of the control input u(k) at time k (the instant of interest in the control problem) into several intervals and identify a local model that is linear in u(k) within each interval. Based on the Taylor’s theorem, a theoretical upper bound for the approximation error of the model structure can also be obtained. With the proposed multiple model architecture, a switching control algorithm is derived to control nonlinear systems based on the weighted one-step-ahead predictive control method and constrained optimization techniques. Both simulation studies and experimental results demonstrate the effectiveness of the proposed multiple model architecture and switching control algorithm. 

Short Biograph of the speaker:  

C. Xiang received the B.S. degree in mechanical engineering from Fudan University, China in 1991; M.S. degree in mechanical engineering from the Institute of Mechanics, Chinese Academy of Sciences in 1994; and Ph.D. degree in electrical engineering from Yale University in 2000.   From 2000 to 2001 he was a financial engineer at Fannie Mae, Washington D.C..  

He has been with the National University of Singapore since 2001. At present, he is the Associate Professor with the Department of Electrical and Computer Engineering, the National University of Singapore. His research interests include pattern recognition, intelligent control and systems biology.