A dynamically-constructed fuzzy neural controller for direct model reference adaptive control of multi-input-multi-output nonlin

Conventional industrial control systems are in majority based on the single-input-single-output design principle with linearized models of the processes. However, most industrial processes are nonlinear and multivariable with strong mutual interactions between process variables that often results in large robustness margins, and in some cases, extremely poor performance of the controller. To improve control accuracy and robustness to disturbances and noise, new design strategies are necessary to overcome problems caused by nonlinearity and mutual interactions. We propose to use a dynamicallyconstructed, feedback fuzzy neural controller (DCF-FNC) from the input

• Type: Journal
• Year: 2002
• Where: SOCO
• Authors: Yakov Frayman, Lipo Wang
• Keywords: Fuzzy Neural Controller, Mutual Interactions, Pid Controller, SOCO 2002, Software Engineering