Abstract:
Application of fuzzy logic is a powerful approach that could be applied in a large
number of disciplines, starting with engineering control systems, as shown here,
but also in other business areas. After a short introduction to fuzzy logic, its
application for adaptive cruise control (ACC) is presented. ACC is a driver
assistance feature that deals with the problem of speed control, while keeping
the safe distance from the vehicle ahead. In the hierarchy of autonomous
vehicles autonomy levels, as defined by Society of Automotive Engineers (SAE)
International, adaptive cruise control appears in the vehicles at the level 1 and
above. We developed a fuzzy logic controller where controlled variables are
speed and distance. Input variables include weather conditions, style or mode of
driving, vehicle speed and steering angle. A large number of input variables
improve control but lead to a large fuzzy rules table. Because of that, in the
design presented here, a tree of connected fuzzy inference systems (FIS) is
applied. Fuzzy inference systems with a smaller number of variables are
developed, algorithms explained, rule base defined, and obtained control
surfaces presented. This approach requires less processing time enabling real
time applications. Since the rules are defined based on drivers’ experiences,
fuzzy logic control systems make decisions in the same way as humans do, i.e.,
as experience drivers. This paper gives a comprehensive presentation of a novel
cascaded fuzzy system development. This novel design also involves algebraic
subtraction performed through a FIS subsystem.
