Fuzzy Logic based Autonomous Parking Systems -- Part III: A Fuzzy Decision Tree System

TL;DR

This paper introduces a hybrid fuzzy controller with a fuzzy decision tree for autonomous parking, enhancing robustness and efficiency in noisy environments through a novel control system design and simulation validation.

Contribution

It presents a new hybrid fuzzy controller integrating a fuzzy decision tree to improve robustness and performance in autonomous parking systems.

Findings

Faster convergence to optimal parking trajectory.

Minimal deviation from desired parking path.

Enhanced robustness against environmental noise.

Abstract

This paper proposes a robust design of Hybrid Fuzzy Controller for speed and steering angle control in an Intelligent Autonomous Parking System (IAPS). The Hybrid Fuzzy Controller consists of a Base Fuzzy Controller (BFC) and a Supervisory Fuzzy Decision Tree Controller (SFDTC). The BFC evolves from previous work on fuzzy logic control for unmanned parking and it ensures that optimal parking trajectory is achieved with minimal computational cost. SFDTC further increases the system robustness when there is noise in the operating environment. The design of SFDTC combines Decision Tree theory and fuzzy inference mechanism. A data training process is also formulated to achieve better control performance. As a result, IAPS equipped with the new Hybrid Fuzzy Controller with Fuzzy Decision Tree (HFC-FDT) demonstrates optimal performance with faster convergence and minimal deviation from optimal parking trajectory. The detailed design of Supervisory Fuzzy Decision Tree Controller is presented in this paper with a MATLAB simulated experiment which concludes the superior performance of the new design.