An adaptive fuzzy logic controller for intelligent networking and control

In this thesis, we present a fuzzy logic control scheme to regulate the flow of traffic approaching a set of intersections. An adaptive Fuzzy Logic Traffic Controller (FLTC) is used to adjust the green phase split of the north-south and east-west approaches of a set of traffic signals based on the actual traffic approaching the intersection. Each intersection is coordinated with its neighbouring intersections by adjusting the offset of the local intersection. The offset is adjusted by a local fuzzy logic controller loacted at each intersection. A new fuzzy control scheme, using a supervisory Fuzzy Logic Controller, is also proposed for adjusting the offset. The fuzzy knowledge base of the supervisory Fuzzy Logic Controller is automatically generated by Genetic Algorithms (GAs). The fuzzy rules generated by the integrated Fuzzy Logic and Genetic Algorithm architecture is found to be effective in optimising the traffic flow. The effectiveness of the above fuzzy control scheme is established through simulations of the traffic flow approaching an isolated intersection, two adjacent intersections, and a set of three intersections. The superiority of adjusting offset using a supervisory fuzzy logic controller is established through simulations.

Grantor dc:publisher

Edith Cowan University, Research Online, Perth, Western Australia

Year dc:date

1996

Author dc:creator

• Nainar, Irshad

• Fuzzy logic
• Computer networks.
• Artificial Intelligence and Robotics
• Computer Sciences
• OS and Networks

Repository record dc:identifier

https://ro.ecu.edu.au/theses/1466

OAI identifier oai:identifier

oai:ro.ecu.edu.au:theses-2467