Fuzzy Logic based Autonomous Parking Systems -- Part IV: A Multiple-Model Adaptive Neural-Fuzzy Controller

TL;DR

This paper introduces a novel multiple-model adaptive neural-fuzzy controller for autonomous parking, combining neural network identification with fuzzy logic to enhance robustness and performance in vehicle control tasks.

Contribution

It presents a new integrated control framework that improves robustness and adaptability in autonomous parking systems without needing prior plant model knowledge.

Findings

Achieves faster and smoother convergence in vehicle control.

Demonstrates robustness under various disturbances.

Outperforms previous control methods in experiments.

Abstract

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.