Multi-model adaptive control based on fuzzy neural networks

摘要: In practical applications, especially in the industrial field, some uncertain factors often lead abrupt changes of system parameters. this paper, multi-model adaptive control (MMAC) based on fuzzy neural networks (FNN) is suggested to identify and discrete-time nonlinear systems such environment. Stable learning algorithms for which are robust any bounded uncertainty applied during identification control. The procedure MMAC different model sets index function given, stability proved. This kind FNN can improve transient response greatly case training process guaranteed. simulation results show that performance better than traditional controllers when parameters have changed abruptly.