DocumentCode :
1516456
Title :
An ANFIS controller for the car-following collision prevention system
Author :
Mar, Jeich ; Lin, Feng-Jie
Author_Institution :
Dept. of Electr. Eng., Yuan-Ze Univ., Taoyuan, Taiwan
Volume :
50
Issue :
4
fYear :
2001
fDate :
7/1/2001 12:00:00 AM
Firstpage :
1106
Lastpage :
1113
Abstract :
This paper presents a controller based on an adaptive network fuzzy inference system (ANFIS) for the car-following collision prevention system to nonlinearly control the speed of the vehicle. The distance and speed relative to the car in front are measured by a radar sensor and applied to the controller. The output acceleration or deceleration rate of the controller is based on the characteristics of the vehicles. The initial input and output membership functions and 25 rules of ANFIS are constructed by a fuzzy inference system (FIS). The design method of the reference signals, which is used to update on-line the consequent parameters of ANFIS according to recursive least square (RLS) algorithm, are proposed. The presented ANFIS controller can solve the problems of the oscillations for final distance between the leading vehicle (LV) and the following vehicle (FV) and relative speed. The required processing time to achieve safe distance between the LV and the FV is about 7-8 s, which is faster than the other models. The ANFIS controller of the car-following collision prevention system proposed in this paper can provide a safe, reasonable, and comfortable drive
Keywords :
adaptive control; automotive electronics; collision avoidance; fuzzy control; inference mechanisms; least mean squares methods; nonlinear control systems; road vehicle radar; transport control; velocity control; adaptive network fuzzy inference system; car-following collision prevention system; following vehicle; fuzzy inference system; leading vehicle; nonlinear speed control; output acceleration; output deceleration; radar sensor; recursive least square algorithm; Adaptive control; Adaptive systems; Control systems; Fuzzy control; Fuzzy neural networks; Fuzzy systems; Nonlinear control systems; Programmable control; Vehicles; Velocity measurement;
fLanguage :
English
Journal_Title :
Vehicular Technology, IEEE Transactions on
Publisher :
ieee
ISSN :
0018-9545
Type :
jour
DOI :
10.1109/25.938584
Filename :
938584
Link To Document :
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