The appearance of anti-lock braking systems (ABS) and traction control systems
(TCS) have been some of the most major developments in vehicle safety. These systems have
been evolving since their origin, always keeping the same objective, by using increasingly
sophisticated algorithms and complex brake and torque control architectures. The aim of this
work is to develop and implement a new control model of a traction control system to be
installed on a motorcycle, regulating the slip in traction and improving dynamic performance of
two-wheeled vehicles. This paper presents a novel traction control algorithm based on the use of
Artificial Neural Networks (ANN) and Fuzzy Logic. An ANN is used to estimate the optimal
slip of the surface the vehicle is moving on. A fuzzy logic control block, which makes use of the
optimal slip provided by the ANN, is developed to control the throttle position. Two control
blocks have been tuned. The first control block has been tuned according to the experience of an
expert operator. The second one has been optimized using Evolutionary Computation (EC).
Simulation shows that the use of EC can improve the fuzzy logic based control algorithm,
obtaining better results than those produced with the control tuned only by experience.
Buscar en Dimension