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This paper describes a fuzzy logic (FL) approach to the design, implementation, and tuning of an expert knowledge-based TCS for a four-wheel drive vehicle. Military and commercial mutual interests in TCS technology are highlighted as the underlying motivation for this government, industry, academia cooperative program. Coordinated parallel efforts to model the TCS equipped vehicle and to perform basic on-vehicle TCS experiments provided additional information to augment the knowledge obtained from a study of commercial TCSs and the tire traction literature. The general traction control problem is discussed along with the hardware considerations for a TCS. The design and integration of the resulting FL-based TCS are described along with a representative sample of the test results documenting the system's performance.

Accurate information of the absolute speed of a vehicle, when available, can be vital in simplifying the control laws of an anti-lock braking system (ABS) and auto-traction system (ATS). A current meter for measuring the speed of a vehicle is to multiply the measured wheel rotation rate to the wheel radius. The approach often includes abrupt unpredictable errors due to slip and skid of wheels and a biased error due to the steady state slip. These errors are sources of difficulty in the implementation of an ABS that is based on the absolute speed of the vehicle. This paper describes an accurate rule-based Kalman filtering technique for estimating the absolute speed of a vehicle. The enhanced accuracy is achieved by employing an additional accelerometer to complement the wheel speed-based speedometer. The accelerometer measures the acceleration of the vehicle in its forward direction and may be corrupted as well by high frequency noise.