Cascaded Fuzzy Logic for Adaptive Cruise Control

Milan Simic

doi:10.47981/j.mijst.10(01)2022.320(33-40)

Milan Simic School of Engineering, RMIT University, Australia

DOI: https://doi.org/10.47981/j.mijst.10(01)2022.320(33-40)

Keywords: Fuzzy logic control, Adaptive cruise control, Cascade controller, Road vehicles, Driver assistance

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.

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