  In this paper, a Multiple Models Adaptive Fuzzy Logic Controller (MM-AFLC)
with Neural Network Identification is designed to control the unmanned vehicle
in Intelligent Autonomous Parking System. The objective is to achieve robust
control while maintaining a low implementation cost. The proposed controller
design incorporates the following control theorems -- non-linear system
identification using neural network, fuzzy logic control, adaptive control as
well as multiple models adaptation. Such integration ensures superior
performance compared to previous work. The generalized controller can be
applied to different systems without prior knowledge of the actual plant model.
In the intelligent autonomous parking system, the proposed controller can be
used for both vehicle speed control and steering wheel turning. With a multiple
model adaptive fuzzy logic controller, robustness can be also assured under
various operating environments regardless of unpredictable disturbances. Last
but not least, comparative experiments have also demonstrated that systems
equipped with the new controller are able to achieve faster and smoother
convergence.
