TY - GEN
T1 - Statistic information tracking of non-gaussian systems
T2 - 2009 IEEE International Conference on Networking, Sensing and Control, ICNSC 2009
AU - Guo, Lei
AU - Yi, Yang
AU - Wang, Hong
PY - 2009
Y1 - 2009
N2 - A new type of data-driven control framework for Non-Gaussian stochastic systems is established in this paper. Different from the traditional feedback style, the driven information for tracking problem is the statistic information set (SIS) of the output rather than the output value. The set of statistical information (including the moments and the entropy) or probability density functions (PDFs) of the output are the measured information and the controlled objective. Under this framework, a mixed two-step adaptive neural network (NN) modeling is established with combining a static NN for description of the statistic information or PDFs and a dynamic one for identification of the relationship between input and output weight vectors. An adaptive PI tracking controller based on the proposed dynamic NNs is designed so as to track a target stochastic distribution. Finally, simulation results on a model in paper-making processes are given to demonstrate the effectiveness.
AB - A new type of data-driven control framework for Non-Gaussian stochastic systems is established in this paper. Different from the traditional feedback style, the driven information for tracking problem is the statistic information set (SIS) of the output rather than the output value. The set of statistical information (including the moments and the entropy) or probability density functions (PDFs) of the output are the measured information and the controlled objective. Under this framework, a mixed two-step adaptive neural network (NN) modeling is established with combining a static NN for description of the statistic information or PDFs and a dynamic one for identification of the relationship between input and output weight vectors. An adaptive PI tracking controller based on the proposed dynamic NNs is designed so as to track a target stochastic distribution. Finally, simulation results on a model in paper-making processes are given to demonstrate the effectiveness.
UR - https://www.scopus.com/pages/publications/70349132017
U2 - 10.1109/ICNSC.2009.4919266
DO - 10.1109/ICNSC.2009.4919266
M3 - 会议稿件
AN - SCOPUS:70349132017
SN - 9781424434923
T3 - Proceedings of the 2009 IEEE International Conference on Networking, Sensing and Control, ICNSC 2009
SP - 170
EP - 175
BT - Proceedings of the 2009 IEEE International Conference on Networking, Sensing and Control, ICNSC 2009
Y2 - 26 March 2009 through 29 March 2009
ER -