Sliding Mode ANFIS-Based MIMO Fuzzy Neural Network Control for Robotic Systems
This paper develops a design methodology of sliding mode
ANFIS-Based multi-inputs multi-outputs (MIMO) fuzzy neural
network (AMFNN) control for robotic systems. This control
system consists of a sliding mode (SM) controller and an
AMFNN controller. The SM controller is used to deal with uncertain
parts of system dynamics and external disturbances and
the AMFNN controller is served as a controller approaching the
ideal controller of SM controller to stabilize the system. The
ANFIS-Based laws of the AMFNN parameters are derived so
that the stability and convergence of the system’s parameters of
AMFNN can be guaranteed. The simulation results reveal that
the better performances are possessed by the proposed AMFNN
control compared with the adaptive fuzzy neural network
(AFNN) control and state feedback control.
Keywords: ANFIS, multi-inputs multi-outputs (MIMO), fuzzy neural network, Robotic system, Sliding mode control.
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ABOUT THE AUTHOR
Yi-Jen Mon
Associated Prof.
Yi-Jen Mon
Associated Prof.
