Traction control systems are a fundamental active safety
equipment of vehicles; they control wheel slip when excessive
torque is applied on driving wheels, helping the driver to bring
the vehicle under control and improving handling and stability
when starting or accelerating and especially under poor or
slippery road conditions. The aim of this work is to develop a
parameter estimation block for further development of an
intelligent traction control system. To evaluate the performance
of the proposed estimation algorithm, estimated variables are
compared making use of BikeSim 2.0 ®. Parameter estimation
was performed using an extended Kalman filter optimized
using genetic algorithms. Using an artificial neural network, the
slip that maximizes the tire-road friction coefficient is
identified.
