Backward Fuzzy Driving Control for 64 Off-Road Vehicles University Of Illinois Urbana-Champaign

Format:

Book

Conference/Event

Author/Creator:

Dekhterman, Samuel R., author.

Sreenivas, Ramavarapu S., author.

Norris, William R., author.

Patterson, Albert E., author.

Soylemezoglu, Ahmet, author.

Nottage, Dustin, author.

Conference Name:

WCX SAE World Congress Experience (2026-04-14 : Detroit, Michigan, United States)

Language:

English

Subjects (All):

Off-highway vehicles and equipment.

Control systems.

Autonomous vehicles.

Fuzzy logic.

Architecture.

Navigation and guidance systems.

Sensors and actuators.

Calibration.

Traction.

Local Subjects:

Off-highway vehicles and equipment.

Control systems.

Autonomous vehicles.

Fuzzy logic.

Architecture.

Navigation and guidance systems.

Sensors and actuators.

Calibration.

Traction.

Physical Description:

1 online resource

Place of Publication:

Warrendale, PA SAE International 2026

Summary:

Off-road autonomous vehicle systems must be able to operate across unstructured and variable terrain while avoiding obstacles. This presents significant challenges in vehicle and control system design, especially for less conventional platforms such as 64 vehicles. While forward driving autonomy has developed and matured in recent years, effective reverse navigation remains an under-explored area of vehicle co-design. Reversing 64 vehicles have limited rear steering authority, an extended wheelbase, and asymmetric traction, which introduce complex dynamics into any control system that is used. To address this need, a robust and experimentally validated fuzzy logic control architecture for 64 reverse navigation was developed during the course of this project. This architecture incorporates both near-field and long-range path data with adaptive outputs controlling steering and velocity based on a rule base that covers the whole vehicle state space. This method has low computational cost and is robust to terrain changes, wheel slip, and actuator lag. To accomplish this, the controller coevolves with the vehicle design parameters, making this an effective co-design strategy. The vehicle design constraints are embedded into the controller through constraint-aware membership functions and rule tuning, reducing the need for terrain-specific calibration. The architecture is modular and scalable across numerous similar platforms, supporting rapid reconfiguration and vehicle design exploration for future autonomous off-road vehicles such as those used in expeditionary environments

Notes:

Vendor supplied data

Access Restriction:

Restricted for use by site license