Model-reference adaptive control based on neurofuzzy networks

Model reference adaptive control (MRAC) is a popular approach to control linear systems, as it is relatively simple to implement. However, the performance of the linear MRAC deteriorates rapidly when the system becomes nonlinear. In this paper, a nonlinear MRAC based on neurofuzzy networks is derived. Neurofuzzy networks are chosen not only because they can approximate nonlinear functions with arbitrary accuracy, but also they are compact in their supports, and the weights of the network can be readily updated on-line. The implementation of the neurofuzzy network-based MRAC is discussed, and the local stability of the system controlled by the proposed controller is established. The performance of the neurofuzzy network-based MRAC is illustrated by examples involving both linear and nonlinear systems. © 2004 IEEE.published_or_final_versio

Neurofuzzy network based adaptive integral control

A self-tuning integral controller with offset removing ability using neurofuzzy methodology is derived for nonlinear control purpose. Controller Auto-Regressive Integrated Moving Average (CARIMA) model is used, and the control law produces integral control terms in a natural way. Neurofuzzy networks are chosen to implement the direct self-tuning nonlinear control. The performance of the self-tuning neurofuzzy controller is illustrated in detail by simulation examples involving both linear and nonlinear systems.link_to_subscribed_fulltex