Yaw Rate Control of Active Front Steering Based on Fuzzy-logic Controller

2010 ◽  
Author(s):  
Qiang Li ◽  
Guobiao Shi ◽  
Yi Lin ◽  
Jie Wei
Keyword(s):  
Fuzzy Logic ◽  
Rate Control ◽  
Fuzzy Logic Controller ◽  
Yaw Rate ◽  
Active Front Steering

Design of an Integrated AFS/ACD Control System to Enhance 4WD Vehicles Dynamics and Stability

2010 ◽  
Author(s):  
Avesta Goodarzi ◽  
Samaneh Arabi ◽  
Ebrahim Esmailzadeh
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Fuzzy Logic Controller ◽  
Traction Force ◽  
Slip Angle ◽  
Force Distribution ◽  
Vehicle Stability ◽  
Handling Performance ◽  
Yaw Rate ◽  
The Ideal

An integrated vehicle dynamic control system for a four-wheel drive vehicle with an active front steering (AFS) and active centre differential (ACD), based on fuzzy logic control, is developed to improve the vehicle stability and its handling performance. The control system has a hierarchical structure consisting of two layers. A fuzzy logic controller is used in the upper layer to keep the yaw rate in its desired value. The yaw rate error and the side slip angle are applied to the upper controlling layer as the inputs, where the desired traction torque transfer ratio and the steering angle correction of the front wheels are the outputs. However, the ideal control effectors could not directly be the control inputs for the centre differential. Therefore, in the lower control loop, one should map the ideal control effectors to the physical control inputs for the centre differential by optimum dynamic traction force distribution. A nonlinear eight degree-of-freedom (DOF) vehicle model with the traction force distribution being utilized by a PI controller is considered. The simulation results illustrate considerable improvements have been achieved for the vehicle stability and handling performance through the integrated AFS/ACD control system.

Design of Fuzzy Logic Controller for Four Wheel Steering System

2005 ◽  
Author(s):  
Evren Ozatay ◽  
Samim Y. Unlusoy ◽  
Murat A. Yildirim
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Control Strategies ◽  
Steering System ◽  
Front Wheel ◽  
Sideslip Angle ◽  
Yaw Rate ◽  
Nonlinear Vehicle Model ◽  
Three Degree Of Freedom ◽  
Vehicle Dynamics Control

Integration of the driver’s steering input together with the four-wheel steering system (4WS) in order to improve the vehicle’s dynamic behavior with respect to yaw rate and body sideslip angle is possible with intelligent vehicle dynamics control systems. The goal of this study is to develop a fuzzy logic controller for this purpose. In the first stage of the study, a three-degree of freedom nonlinear vehicle model including roll dynamics is developed. The Magic Formula is applied in order to formulate the nonlinear characteristics of the tires. In the design of the fuzzy logic controller, a two-dimensional rule table is created based on the error and on the change in the error of sideslip angle, which is to be minimized. Fuzzy logic controlled model is then compared with front wheel steering vehicle and the vehicles having different control strategies that have previously been studied in literature. Simulations indicate that fuzzy logic controlled vehicle can provide zero body sideslip angle in transient motion and quick response in terms of yaw rate during steady state cornering and lane change maneuvers.

Application Fuzzy Logic System for Accurate Sheet Trim Shower Position

2019 ◽  
Vol 3 (1) ◽  
pp. 186-192
Author(s):  
Yudi Wibawa
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Dc Motor ◽  
Automation System ◽  
Paper Making ◽  
System A ◽  
Accurate Position ◽  
And Performance ◽  
Better Than ◽  
Logic System

This paper aims to study for accurate sheet trim shower position for paper making process. An accurate position is required in an automation system. A mathematical model of DC motor is used to obtain a transfer function between shaft position and applied voltage. PID controller with Ziegler-Nichols and Hang-tuning rule and Fuzzy logic controller for controlling position accuracy are required. The result reference explains it that the FLC is better than other methods and performance characteristics also improve the control of DC motor.

STABILISASI TEGANGAN KELUARAN BUCK CONVERTER DENGAN METODE FUZZY LOGIC CONTROLLER

JURNAL ELTEK ◽  
2018 ◽  
Vol 16 (2) ◽  
pp. 125
Author(s):  
Oktriza Melfazen
Keyword(s):  
Fuzzy Logic ◽  
Steady State ◽  
Rise Time ◽  
Fuzzy Logic Controller ◽  
Buck Converter ◽  
Settling Time

Buck converter idealnya mempunyai keluaran yang stabil, pemanfaatandaya rendah, mudah untuk diatur, antarmuka yang mudah dengan pirantiyang lain, ketahanan yang lebih tinggi terhadap perubahan kondisi alam.Beberapa teknik dikembangkan untuk memenuhi parameter buckconverter. Solusi paling logis untuk digunakan pada sistem ini adalahmetode kontrol digital.Penelitian ini menelaah uji performansi terhadap stabilitas tegangankeluaran buck converter yang dikontrol dengan Logika Fuzzy metodeMamdani. Rangkaian sistem terdiri dari sumber tegangan DC variable,sensor tegangan dan Buck Converter dengan beban resistif sebagaimasukan, mikrokontroler ATMega 8535 sebagai subsistem kontroldengan metode logika fuzzy dan LCD sebagai penampil keluaran.Dengan fungsi keanggotaan error, delta error dan keanggotaan keluaranmasing-masing sebanyak 5 bagian serta metode defuzzifikasi center ofgrafity (COG), didapat hasil rerata error 0,29% pada variable masukan18V–20V dan setpoint keluaran 15V, rise time (tr) = 0,14s ; settling time(ts) = 3,4s ; maximum over shoot (%OS) = 2,6 dan error steady state(ess) = 0,3.

Performance Comparison of MANET, VANET and FANET

2018 ◽  
Vol 8 (5) ◽  
pp. 132
Author(s):  
Aarti Sahu ◽  
Laxmi Shrivastava
Keyword(s):  
Fuzzy Logic ◽  
Wireless Network ◽  
Packet Loss ◽  
Ad Hoc Network ◽  
Fuzzy Logic Controller ◽  
Ad Hoc ◽  
Mobile Ad Hoc Network ◽  
Performance Comparison ◽  
Fuzzy Rules ◽  
Mobile Ad Hoc

A wireless ad hoc network is a decentralized kind of wireless network. It is a kind of temporary Computer-to-Computer connection. It is a spontaneous network which includes mobile ad-hoc network (MANET), vehicular ad-hoc network (VANET) and Flying ad-hoc network (FANET). Mobile Ad Hoc Network (MANET) is a temporary network that can be dynamically formed to exchange information by wireless nodes or routers which may be mobile. A VANET is a sub form of MANET. It is an technology that uses vehicles as nodes in a network to make a mobile network. FANET is an ad-hoc network of flying nodes. They can fly independently or can be operated distantly. In this research paper Fuzzy based control approaches in wireless network detects & avoids congestion by developing the ad-hoc fuzzy rules as well as membership functions.In this concept, two parameters have been used as: a) Channel load b) The size of queue within intermediate nodes. These parameters constitute the input to Fuzzy logic controller. The output of Fuzzy logic control (sending rate) derives from the conjunction with Fuzzy Rules Base. The parameter used input channel load, queue length which are produce the sending rate output in fuzzy logic. This fuzzy value has been used to compare the MANET, FANET and VANET in terms of the parameters Throughput, packet loss ratio, end to end delay. The simulation results reveal that usage of Qual Net 6.1 simulator has reduced packet-loss in MANET with comparing of VANET and FANET.

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