scholarly journals Urban Traffic Signals Timing at Four-Phase Signalized Intersection Based on Optimized Two-Stage Fuzzy Control Scheme

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Taiping Jiang ◽  
Zili Wang ◽  
Fuyang Chen
Keyword(s):  
Fuzzy Control ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Differential Evolution Algorithm ◽  
Arrival Rate ◽  
Fixed Time ◽  
Urban Traffic ◽  
Two Stage ◽  
Phase Selection ◽  
Vehicle Delay

This paper proposes a signal timing scheme through a two-stage fuzzy logic controller. The controller first determines the signal phase and then adjusts the green time. At the first stage, the adaptive membership function of vehicle arrival rate is improved to adapt to the changing traffic flow. In addition to arrival rate and queue vehicles, a specific phase order rule is considered to avoid disordered phase selection in fuzzy control. At the second stage, the green time detection module decides whether to extend the current green time or switch phases every few seconds and the vehicle arrival rate is not required as the input to controller in real-time detection. Differential evolution algorithm with low space complexity and fast convergence is applied to optimize the fuzzy rules for avoiding artificial uncertainty. Simulation experiments are designed to compare traditional fuzzy controller, fixed-time controller, and fuzzy controller without flow prediction. Results show that the current proposed method in this paper can reduce vehicle delay significantly.

Design and Optimization of Urban Signal Fuzzy Controller Based on IQPSO

2014 ◽  
Vol 527 ◽  
pp. 152-155
Author(s):  
Wei Li ◽  
Xin Bi ◽  
Yun Xia Cao ◽  
Jin Song Du
Keyword(s):  
Fuzzy Control ◽  
Control Method ◽  
Fuzzy Controller ◽  
Fixed Time ◽  
Traffic Signal ◽  
Urban Traffic ◽  
Average Delay ◽  
Phase Switching ◽  
Design And Optimization ◽  
Time Control

In order to overcome the shortcomings of traffic signal fixed-time control method, a fuzzy control algorithm for urban traffic signal is proposed. The signal phase switching order is adjustable. The improved quantum particle swarm optimization(QPSO) is also introduced to optimize fuzzy control rules of traffic signal controller. Take four-phase traffic signal commonly used in current practice for example. Compared with traffic signal fixed-time control and single fuzzy control method, the control method put forward in this paper can reduce the vehicles’ average delay time in junction. The simulation results show that the proposed algorithm is proved to be an effective and practicable method for urban traffic self-adaptive control.

scholarly journals Traffic Control Prediction Design Based on Fuzzy Logic and Lyapunov Approaches to Improve the Performance of Road Intersection

Processes ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 2205
Author(s):  
Sadiqa Jafari ◽  
Zeinab Shahbazi ◽  
Yung-Cheol Byun
Keyword(s):  
Traffic Control ◽  
Fuzzy Controller ◽  
Fixed Time ◽  
Urban Traffic ◽  
Urban Transport ◽  
Traffic Light ◽  
Traffic System ◽  
Input Variables ◽  
Space Dynamics ◽  
Takagi Sugeno

Due to the increasing use of private cars for urbanization and urban transport, the travel time of urban transportation is increasing. People spend a lot of time in the streets, and the queue length of waiting increases accordingly; this has direct effects on fuel consumption too. Traffic flow forecasts and traffic light schedules were studied separately in the urban traffic system. This paper presents a new stable TS (Takagi–Sugeno) fuzzy controller for urban traffic. The state-space dynamics are utilized to formulate both the vehicle’s average waiting time at an isolated intersection and the length of queues. A fuzzy intelligent controller is designed for light control based upon the length of the queue, and eventually, the system’s stability is proved using the Lyapunov theorem. Moreover, the input variables are the length of queue and number of input or output vehicles from each lane. The simulation results describe the appearance of the proposed controller. An illustrative example is also given to show the proposed method’s effectiveness; the suggested method is more efficient than both the conventional fuzzy traffic controllers and the fixed time controller.

scholarly journals Investigation of the Possibility of Building a Neural Fuzzy Logic Controller with Discrete Terms for Controlling and Automating Oil and Gas Engineering Facilities

2021 ◽  
Vol 19 (3) ◽  
pp. 105-110
Author(s):  
A. M. Sagdatullin ◽  
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Control System ◽  
Fuzzy Control ◽  
Control Systems ◽  
Fuzzy Logic Controller ◽  
Oil And Gas ◽  
Fuzzy Controller ◽  
Neuro Fuzzy ◽  
Classical Control

The issue of increasing the efficiency of functioning of classical control systems for technological processes and objects of oil and gas engineering is investigated. The relevance of this topic lies in the need to improve the quality of the control systems for the production and transportation of oil and gas. The purpose of the scientific work is to develop a neuro-fuzzy logic controller with discrete terms for the control and automation of pumping units and pumping stations. It is noted that fuzzy logic, neural network algorithms, together with control methods based on adaptation and synthesis of control objects, make it possible to learn the automation system and work under conditions of uncertainty. Methods for constructing classical control systems are studied, the advantages and disadvantages of fuzzy controllers, as the main control system, are analyzed. A method for constructing a control system based on a neuro-fuzzy controller with discrete terms in conditions of uncertainty and dynamic parameters of the process is proposed. The positive features of the proposed regulator include a combination of fuzzy reasoning about a technological object and mathematical predictive models, a fuzzy control system gains the possibility of subjective description based on neural network structures, as well as adaptation to the characteristics of the object. The graph of dependence for the term-set of the controlled parameter on the degree of membership is presented. A possible implementation of tracking the triggering of one of the rules of the neuro-fuzzy system in the format of functional block diagrams is presented. The process of forming an expert knowledge base in a neuro-fuzzy control system is considered. For analysis, a graph of the dependence of the output parameter values is shown. According to the results obtained, the deviation of the values for the model and the real process does not exceed 18%, which allows us to speak of a fairly stable operation of the neuro-fuzzy controller in automatic control systems.

scholarly journals Desain Model Fuzzy Control UAV Berbasis Matlab/Simulink pada Arduino Flight Controller

2018 ◽  
Vol 2 (2) ◽  
pp. 19
Author(s):  
Muchamad Malik ◽  
Aan Burhanuddin
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Fuzzy Control ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
The Past ◽  
Aerial Vehicle ◽  
Hardware In Loop ◽  
System Designs ◽  
Vehicle Platform

<p><em>Quadrocopter is an aerial vehicle platform that has become very popular among researchers from the past because it has advantages compared to conventional helicopters. The quadrocopter design is very simple and unique but seen from an unstable aerodynamic standpoint. From existing research, researchers have proposed many control system designs for quadrocopter. In this study, the author presents a fuzzy logic controller for quadrocopter. The method in this research is by designing hardware. After that the design for fuzzy controllers. Then the designed fuzzy controller is tested in the Hardware In Loop (HIL) setting. The experimental results and validation of the controller application functions are considered satisfactory and it is concluded that it is possible to stabilize quadrocopter with fuzzy logic controller.</em></p>

Congestion Control by Reducing Wait time at the traffic junction using fuzzy logic controller

2020 ◽  
Vol 10 ◽  
Author(s):  
Manpreet Singh Bhatia ◽  
Alok Aggarwal
Keyword(s):  
Real Time ◽  
Traffic Congestion ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Detection System ◽  
Green Light ◽  
Wait Time ◽  
Arrival Rate ◽  
Traffic Signal ◽  
Traffic Light

Traffic congestion is one of the most severe problems especially in metro cities due to ever increasing number of vehicles on roads by 20% per year even with well-planned road management system and sufficient infra. Most of the existing traffic signal controllers use fixed cycle type, giving a constant green/red/yellow phase for each traffic signal cycle. These traditional controllers cannot adapt the dynamics of traffic at real time which a traffic man can do. Deploying traffic men at every traffic light junction is not feasible due to manpower shortage and cost considerations. In this work a three input fuzzy controller is proposed which can adapt the dynamics of real time traffic and reduce the congestion at the traffic light junction. Proposed fuzzy controller has three inputs namely; queue length, arrival rate and peak hours and one output parameter, time extension which is to be controlled by the use of the three input parameters. All four lanes have been allocated a fixed green signal time of 60 seconds at the start. Extension/decrease of the green light is done dynamically with ±28 seconds. Compared to conventional fixed cycle type, proposed approach gives a minimum improvement of 6% and a maximum of 47% depending on various traffic conditions at the junction. In terms of CO2 emission improvement of 20% and 42.12% and in terms of fuel consumption improvement of 34.73% and 57.18% has been observed compared to UCONDES (Urban CONgestion DEtection System) and OVMT (Original Vehicular Mobility Trace) respectively.

Research on Urban Traffic Signal Control Method at Single Intersection

2011 ◽  
Vol 361-363 ◽  
pp. 1799-1802 ◽  
Author(s):  
Li Fang Bai ◽  
Jin Xue Xu
Keyword(s):  
Fuzzy Logic Controller ◽  
Control Method ◽  
Fuzzy Controller ◽  
Good Control ◽  
Urban Traffic ◽  
Traffic Signal Control ◽  
Traffic Light ◽  
Control Effect ◽  
Real Time Traffic ◽  
Smooth Flow

A fuzzy logic controller is presented for a four-phase isolated signalized intersection on normal and abnormal conditions. It controls the traffic light timings to ensure smooth flow of traffic with minimal delay, according to the real-time traffic flow information detected by the vehicle detector. A new controller is proposed, in which the fuzzy membership functions are optimized by neural network and the control rules are optimized by genetic algorithm. Results show that the traditional fuzzy controller achieves good control effect and the performance of the controller optimized is better than the traditional one on both normal and abnormal conditions.

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