Distributed traffic signal control using fuzzy logic

We present a distributed approach to traffic signal control, where the signal timing parameters at a given intersection are adjusted as functions of the local traffic condition and of the signal timing parameters at adjacent intersections. Thus, the signal timing parameters evolve dynamically using only local information to improve traffic flow. This distributed approach provides for a fault-tolerant, highly responsive traffic management system. The signal timing at an intersection is defined by three parameters: cycle time, phase split, and offset. We use fuzzy decision rules to adjust these three parameters based only on local information. The amount of change in the timing parameters during each cycle is limited to a small fraction of the current parameters to ensure smooth transition. We show the effectiveness of this method through simulation of the traffic flow in a network of controlled intersections

Capturing hand tremors with a fuzzy logic wheelchair joystick controller

We have designed and built a fuzzy logic wheelchair controller which minimizes the effect of Multiple Sclerosis and tremors. The aim of our project has been to give people with Multiple Sclerosis better control of an electric wheelchair by removing tremors from the joystick signal. The system intercepts the signal from the joystick and then passes it through the fuzzy logic controller. The fuzzy logic identify and eliminate erratic or unusual movements, employing a history mechanism to determine what "unusual" is. The fuzzy logic than outputs a signal which closely represents the intent of the user. This paper reports on the experiments conducted with our prototype wheelchair, using test volunteers with MS, as well as on the design of a new fuzzy controller. Also, we give a brief overview of the variety of recorded tremors. We show that those who have the most severe MS tremors benefit from the system, and are able to control the wheelchair safely

Reduction of axis acceleration of quarter car suspension using pneumatic actuator and active force control technique

This paper presents the design of a control technique applied to the pneumatic active suspension system of a quarter car model using controller with fuzzy logic embedded in the active force control component. The overall control system is decomposed into two loops. In the main loop the desired force signal is calculated using an active force control strategy with a sugeno fuzzy logic element which is being employed to estimate the mass needed to feed the control loop. A Mamdani fuzzy logic controller is implemented in the outer loop to design a force controller such that the desired force signal is achieved in a robust manner. The resulting control strategy known as fuzzy – active force controller (FLC-AFC) is used to control a nonlinear actuator attached between the sprung mass and the unsprung mass of the quarter car model. The performances of the proposed control method were evaluated and later compared to examine the effectiveness in suppressing the vibration effect of the suspension system. Resulting fuzzy active force control gives better results if compared to the fuzzy logic and the passive suspension system

Pengaturan Berat Total Material Yang Keluar Dari Weight Feeder Conveyor Dengan Menggunakan Kontrol Logika Fuzzy

This paper describes about a fuzzy logic control's application on weight feeder conveyor. The plant represented by a third order transfer function. The designed fuzzy logic control had single input and single output. The crisp input of fuzzy logic control was an error signal and the crisp output was a speed of motor. The system was tested by a computer simulation using Matlab. Experiment results showed that fuzzy logic control could work well. The best rise time and settling time achieved by the system were 4 and 6 seconds respectively

Dynamic Left-turn Phase Optimization Using Fuzzy Logic Control

The left-turn movement at an intersection has long been a concern of traffic engineers as it is a major capacity reduction factor. Different left-turn signal phasings have been shown to result in significant differences in delay, intersection capacity, and even safety level. First, past studies about leading and lagging signal phases and signal control application are overviewed. Then this research gives a theoretical analysis of signal left-turn phase operations at both isolated and coordinated signalized intersections, compares the difference in delay based on leading and lagging left-turn signal phase designs, analyzes the influences of traffic control delay components for leading and lagging left-turn, identifies the main control factors, and gives a new model to guide the choosing between the leading and lagging left-turn phases. In the third part of this research, some basic mathematical definitions and rules of fuzzy logic control are described. A four-level fuzzy logic control model is designed. To implement this control model, observed approaching traffic flows are used to estimate relative traffic intensities in the competing approaches. These traffic intensities are then used to determine whether a leading or lagging signal phase should be selected or terminated. Finally, this research designs a dynamic traffic signal left-turn phase control system, and implements the four-level fuzzy logic control model to optimize signalized intersection operation. The performance of this dynamic traffic signal left-turn phase fuzzy logic control system compared favorably in all categories to fixed time control, actuated control, and traditional fuzzy control based on simulation using field data. The results suggest that the proposed dynamic traffic signal left-turn phase fuzzy logic control system is a superior and efficient tool for reducing intersection traffic delay. The study also demonstrated that the successful implementation of the proposed model does not rely on the installation of expensive or complicated equipment

Managing uncertainty in sound based control for an autonomous helicopter

In this paper we present our ongoing research using a multi-purpose, small and low cost autonomous helicopter platform (Flyper ). We are building on previously achieved stable control using evolutionary tuning. We propose a sound based supervised method to localise the indoor helicopter and extract meaningful information to enable the helicopter to further stabilise its flight and correct its flightpath. Due to the high amount of uncertainty in the data, we propose the use of fuzzy logic in the signal processing of the sound signature. We discuss the benefits and difficulties using type-1 and type-2 fuzzy logic in this real-time systems and give an overview of our proposed system

Learning and tuning fuzzy logic controllers through reinforcements

A new method for learning and tuning a fuzzy logic controller based on reinforcements from a dynamic system is presented. In particular, our Generalized Approximate Reasoning-based Intelligent Control (GARIC) architecture: (1) learns and tunes a fuzzy logic controller even when only weak reinforcements, such as a binary failure signal, is available; (2) introduces a new conjunction operator in computing the rule strengths of fuzzy control rules; (3) introduces a new localized mean of maximum (LMOM) method in combining the conclusions of several firing control rules; and (4) learns to produce real-valued control actions. Learning is achieved by integrating fuzzy inference into a feedforward network, which can then adaptively improve performance by using gradient descent methods. We extend the AHC algorithm of Barto, Sutton, and Anderson to include the prior control knowledge of human operators. The GARIC architecture is applied to a cart-pole balancing system and has demonstrated significant improvements in terms of the speed of learning and robustness to changes in the dynamic system's parameters over previous schemes for cart-pole balancing

Rancang Bangun Farming Box Dengan Pengaturan Suhu Menggunakan Fuzzy Logic Controller

Implementation of control systems has been carried out in many fields of science. One of it applications is in the agriculture fields. In this research we implemented a control system on farming in a box. Farming in a box is a system that uses old shipping containers for the purpose of growing plants in any environment. Inside shipping containers is fully assembled hydroponic pipe with air temperature control. In this research was built a little farming box from acryclic to imitate a shipping container. Main focus of this research is design an air temperature control using fuzzy logic controller. Fuzzy logic controller was choosen because many existing farming box use on off controller. In some application, fuzzy logic controller has better performance than on off controller. Farming box temperature is controlled by blowing cool air using an electric fan. In this case, cool air is produced by cold side of peltier. Electric fan speed is controlled by pulse width modulation signal (PWM) that generated from microcontroller. Air temperature data feedback is obtained from DHT 11 sensor that installed in a acrylic box. Sensor is physically connected with microcontroller and Fuzzy logic controller is embedded in microcontroller as an algorithm. Fuzzy logic controller was design with error temperature and error difference as an input, and duty cycle of PWM signal as output. Fuzzy logic controller system performs to reduce the temperature from 31,6 ° C to set poin 28° C in 71 seconds. Steady state error obtained by 1.28% and better than uncontrolled system that obtain steady state error 7,14%

Second Order Integral Fuzzy Logic Control Based Rocket Tracking Control

Fuzzy logic is a logic that has a degree of membership in the vulnerable 0 to 1. Fuzzy logic is used to translate a quantity that is expressed using language. Fuzzy logic is used as a control system because this control process is relatively easy and flexible to design without involving complex mathematical models of the system to be controlled. The purpose of this paper is to present a fuzzy control system implemented in a rocket tracking control system. The fuzzy control system is used to keep the rocket on track and traveling at a certain speed. The signal from the fuzzy logic control system is used to control the rocket thrust. The fuzzy Logic System was chosen as the controller because it is able to work well on non-linear systems and offers convenience in program design. Fuzzy logic systems have a weakness when working on systems that require very fast control such as rockets. With this problem, fuzzy logic is modified by adding second-order integral control to the modified fuzzy logic. The proposed algorithm shows that the missile can slide according to the ramp path at 12 m altitude of 12.78 at 12 seconds with a steady-state error of 0.78 under FLC control, at 10 m altitude of 10.68 at 10 seconds with a steady-state error of 0.68 with control integral FCL, at a height of 4 m is 4.689 at 4 seconds with a steady-state error of 0.689 with a second-order integral control of FCL. The missile can also slide according to the parabolic path with the second-order integral control of FCL at an altitude of 15.47 in the 4th minute with a steady-state error of 0

DC Motor Speed Control Using Mamdani Fuzzy Logic Based on Microcontroller

DC motors are included in the category of motor types that are most widely used both in industrial environments, household appliances to children's toys. The development of control technology has also made many advances from conventional control to automatic control to intelligent control. Fuzzy logic is used as a control system, because this control process is relatively easy and flexible to design without involving complex mathematical models of the system to be controlled. The purpose of this research is to study and apply the fuzzy mamdani logic method to the Arduino uno microcontroller, to control the speed of a DC motor and to control the speed of the fan. The research method used is an experimental method. Global testing is divided into three, namely sensor testing, Pulse Width Modulation (PWM) testing and Mamdani fuzzy logic control testing. The fuzzy controller output is a control command given to the DC motor. In this DC motor control system using the Mamdani method and the control system is designed using two inputs in the form of Error and Delta Error. The two inputs will be processed by the fuzzy logic controller (FLC) to get the output value in the form of a PWM signal to control the DC motor. The results of this study indicate that the fuzzy logic control system with the Arduino uno microcontroller can control the rotational speed of the DC motor as desired