Affiliations: Key Laboratory of Advanced Control of Iron and Steel Process (Ministry of Education), School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, P.R. China
Note: [] Corresponding author. Xiao-li Li, Key Laboratory of Advanced Control of Iron and Steel Process (Ministry of Education), School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, P.R. China. Tel.: +86 13488806991; Fax: +86 1062332905; E-mail: lixiaoli@hotmail.com
Abstract: In practical applications, especially in the industrial field, some uncertain factors often lead abrupt changes of the system parameters. In this paper, multi-model adaptive control (MMAC) based on fuzzy neural networks (FNN) is suggested to identify and control discrete-time nonlinear systems in such environment. Stable learning algorithms for fuzzy neural networks which are robust to any bounded uncertainty are applied during the identification and adaptive control. The procedure of MMAC based on different model sets and index function is given, and the stability of MMAC is proved. This kind of MMAC based on FNN can improve the transient response greatly in case of the stability of training process is guaranteed. The simulation results show that the control performance is better than traditional adaptive controllers when the system parameters have changed abruptly.
