Control of Automobile's Automatic Parking

2011 ◽  
Vol 339 ◽  
pp. 28-31
Author(s):  
Hong Mei
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Control ◽  
Fuzzy Controller ◽  
Steering Wheel ◽  
Experienced Driver ◽  
Parking System ◽  
Automatic Parking ◽  
Conventional Methods ◽  
The Ideal

An automatic parking controller is proposed. Fuzzy control is taken to simulate the action of experienced driver as an alternative to conventional methods. The angle between the midline of the car and ideal path and the distance between the midpoint of the car and the ideal path are taken as the inputs of the fuzzy controller. The angle of the steering wheel is taken as the output of the fuzzy controller. A set of fuzzy logic rules are build for reasoning. With sensors installed in the car to replace people’s eyes and computer to replace people’s brain, the automatic parking system is more precise and quicker than human’s parking. At last, simulation is made and proved the validity of the proposed method.

Research on Fuzzy Control System for Air-Condition in Bus

2012 ◽  
Vol 457-458 ◽  
pp. 998-1001
Author(s):  
Qiu Hua Miao ◽  
Pei Gang Jiao ◽  
Jie Liang
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Fuzzy Control ◽  
Fuzzy Logic Control ◽  
Fuzzy Controller ◽  
Logic Control ◽  
Temperature Signal ◽  
Working Principle ◽  
Fuzzy Logic Technique ◽  
Deflection Rate

In this paper fuzzy logic control is introduced in auto adjust control system of air in bus. Fuzzy controller employs fuzzy control model of two inputs and one output. The paper also introduces the hardware framework, working principle and software of the system, analyzing merits of fuzzy logic technique, putting emphasis on the design of the fuzzy controller. In the controlling process, fuzzy controller samples temperature signal in bus and calculate deflection and deflection rate of temperature, looks up corresponding output in controlling table, and calculates its precision, then fuzzy controller gives related signal to motors and valve, acquiring satisfactory effect.

A Fuzzy Logic Scheme for Lateral Control of an Unmanned Vehicle

2014 ◽  
Vol 1046 ◽  
pp. 250-254
Author(s):  
Yu Fang Zhang ◽  
Xiao Nian Wang ◽  
Ping Jiang ◽  
Jin Zhu
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Fuzzy Controller ◽  
Dynamic Performance ◽  
Small Error ◽  
Steering Wheel ◽  
Tracking Accuracy ◽  
Lateral Control ◽  
Unmanned Vehicle ◽  
Logic Control

The purpose of unmanned vehicle lateral control is to track a desired trajectory in a small error, in order to achieve a stable tracking under different pavements and wind resistance conditions. This paper presents a vehicle lateral control scheme based on fuzzy logic control. A simplified vehicle lateral dynamics model is first obtained by linearizing the original 6-DOF vehicle model, and then the lateral control is decomposed into two modules: steering wheel angle control and steering wheel speed control. Fuzzy logic control for the two modules is developed and simulated by using CarSim and Simulink. The results demonstrate that the fuzzy controller can achieve a high tracking accuracy with a good dynamic performance.

Control of a Fluidized Bed Combustor Using Fuzzy Logic

1996 ◽  
Vol 118 (1) ◽  
pp. 204-209 ◽  
Author(s):  
S. J. Koffman ◽  
R. C. Brown ◽  
R. R. Fullmer
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Control ◽  
Fluidized Bed ◽  
Fuzzy Logic Control ◽  
Fuzzy Controller ◽  
Experimental Results ◽  
Pi Control ◽  
Logic Control ◽  
And Performance ◽  
Fluidized Bed Combustor

Application of fuzzy logic control to a fluidized bed combustor (FBC) is examined. Major aspects of fuzzy control are reviewed, and design of a fuzzy controller for the FBC is described. Selected experimental results are presented, and performance of the fuzzy controller is evaluated through comparisons to results from classical PI control of the combustor.

Simulation for Temperature Control System of Dyeing Machine Based on Fuzzy Control

2011 ◽  
Vol 317-319 ◽  
pp. 1688-1692
Author(s):  
Min Ling Zhao ◽  
Guo Ping Li ◽  
Xiong Bo Ze ◽  
Cheng Kai Ji
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Fuzzy Control ◽  
Temperature Control ◽  
Fuzzy Logic Control ◽  
Fuzzy Controller ◽  
Decisive Role ◽  
Temperature Control System ◽  
Logic Control

In the process of dyeing, the temperature control of dyeing machine plays a decisive role on the stand or fall quality of fabric. The establishment of the traditional PID controller’s parameters needs a lot of test, which brings many inconvenience.Therefore, it is proposed to control dyeing machine temperature by fuzzy controller. Based on the principle of fuzzy logic control, the model of the temperature control system of dyeing machine is built. At the same time, through the fuzzy logic toolbox in matlab software, fuzzy controller of temperature is designed. Then a comparative simulation of the temperature control system of dyeing machine with matlab has been accomplished. Through the analysis of the results, it is concluded that the temperature system can achieve the higher steady precision.

scholarly journals Modeling of automatic control system for air supply to a bio-reactor using fuzzy control

2021 ◽  
Vol 2131 (2) ◽  
pp. 022009
Author(s):  
V F Lubentsov ◽  
E A Shakhrai ◽  
E V Lubentsova
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Steady State ◽  
Fuzzy Control ◽  
Automatic Control ◽  
Automatic Control System ◽  
Fuzzy Controller ◽  
Nonlinear Controller ◽  
Steady State Mode ◽  
Air Supply

Abstract The stages of modeling the automatic control system (ACS) for air supply to aeration with the use of fuzzy control are considered. The investigated control algorithm is based on the combination of a nonlinear controller with approximating control (CAC), whose parameters are corrected using fuzzy logic. The algorithm for correcting the CAC parameters for transient and steady state modes is based on the application of two simple rulebases (RB) with three and five linguistic terms, respectively. As a result, the required speed in the transient mode and accuracy in the steady state mode are provided. It is proved that switching the RB according to the logic of the multi-mode system is less demanding on the number of rules, structure and setting parameters of the membership function than using the extended RB. The differences between the proposed ACS with different BP for the main operating modes of the system are shown. These include: improvement of quality indicators due to the implementation of different BP in different modes; more rigorous justification of the mechanism for ensuring insensitivity to the switching moments of BP when changing modes due to the CAC of the direct circuit of the ACS. Effective implementation of the stages of ACS modeling and fuzzy controller design is possible using the Fuzzy Logic Toolbox system of the Simulink MATLAB modeling environment.

scholarly journals Implementation of Fuzzy Logic Control System on Rotary Car Parking System Prototype

2018 ◽  
Vol 12 (2) ◽  
pp. 706
Author(s):  
Nanang Ismail ◽  
Iim Nursalim ◽  
Hendri Maja Saputra ◽  
Teddy Surya Gunawan
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Steady State ◽  
Fuzzy Control ◽  
Rise Time ◽  
Settling Time ◽  
Inference Model ◽  
Time Value ◽  
Parking System ◽  
State Error

Rotary car parking system (RCPS) is one of the effective parking models used in the metropolitan area because the mechanical parking system is designed vertically to conserve the land usage. This paper discussed the implementation of fuzzy logic with the Sugeno Inference Model on the RCPS miniature control system. The research started with kinematics analysis and a mathematical model was derived to determine the slot position and optimal power requirements for each condition. Furthermore, the Fuzzy Inference model used was the Sugeno Model, taking into account two variables: distance and angle. These two variables were selected because in the designed miniature RCPS there will be rotational changes of rotation and rotation in turn. Variable distance was divided into four clusters, such as Zero, Near, Medium and Far. While the angle variables were divided into four clusters as well, such as Zero, Small, Medium, and Big. The test results on a miniature RCPS consisting of six parking slots showed that fuzzy based control provided better results when compared to conventional systems. Step response on the control system without fuzzy control showed the rise time value of 0.58 seconds, peak time of 0.85 seconds, settling time of 0.89, percentage overshoot of 0.20%, and steady state error of 4.14%. While the fuzzy control system provided the rise time value of 0.54 seconds, settling time of 0.83 seconds, steady state error of 2.32%, with no overshoot.

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 Reinforcement Learning-Based End-to-End Parking for Automatic Parking System

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3996 ◽  
Author(s):  
Peizhi Zhang ◽  
Lu Xiong ◽  
Zhuoping Yu ◽  
Peiyuan Fang ◽  
Senwei Yan ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Tracking Error ◽  
Path Tracking ◽  
Steering Wheel ◽  
Local Optimum ◽  
Training Methods ◽  
Parking System ◽  
Network Output ◽  
Automatic Parking ◽  
End To End

According to the existing mainstream automatic parking system (APS), a parking path is first planned based on the parking slot detected by the sensors. Subsequently, the path tracking module guides the vehicle to track the planned parking path. However, since the vehicle is non-linear dynamic, path tracking error inevitably occurs, leading to inclination and deviation of the parking. Accordingly, in this paper, a reinforcement learning-based end-to-end parking algorithm is proposed to achieve automatic parking. The vehicle can continuously learn and accumulate experience from numerous parking attempts and then learn the command of the optimal steering wheel angle at different parking slots. Based on this end-to-end parking, errors caused by path tracking can be avoided. Moreover, to ensure that the parking slot can be obtained continuously in the process of learning, a parking slot tracking algorithm is proposed based on the combination of vision and vehicle chassis information. Furthermore, given that the learning network output is hard to converge, and it is easy to fall into local optimum during the parking process, several reinforcement learning training methods in terms of parking conditions are developed. Lastly, by the real vehicle test, it is proved that using the proposed method can achieve a better parking attitude than using the path planning and path tracking-based method.

Design Fuzzy Logic Control System of Automatic Irrigation Based on MINI2440 and C8051F410 MCU

2013 ◽  
Vol 739 ◽  
pp. 499-504
Author(s):  
He Xi Zhang ◽  
Dao Cai Chi ◽  
Li Xuan Wang ◽  
Tao Tao Chen ◽  
Yong Tao Wang
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Robust Control ◽  
Fuzzy Control ◽  
Fuzzy Logic Control ◽  
Control Algorithm ◽  
Fuzzy Controller ◽  
Control Mode ◽  
Logic Control ◽  
Controlling System

In this paper, the fuzzy controlling system of automatic irrigation was prepared and designed based on MINI2440 and C8051F410 MCU, in which MATLAB was embedded in C8051F410 MCU while WINCE5.0 was embedded in MINI2440. In the progress, the WINCE5.0 was developed by using C#. All the simulate results of fuzzy controller were shown by SIMLINK in the system. In this paper, we proposed a novel control mode and gave the Robust Control Algorithm of the fuzzy Control System.

scholarly journals Development of a Didactic Educational Tool for Learning Fuzzy Control Systems

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Juan Aguilera-Alvarez ◽  
José Padilla-Medina ◽  
Coral Martínez-Nolasco ◽  
Víctor Samano-Ortega ◽  
Micael Bravo-Sanchez ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Academic Performance ◽  
Fuzzy Control ◽  
Control Systems ◽  
Fuzzy Controller ◽  
Intelligent System ◽  
Knowledge Assessment ◽  
Teaching Tool ◽  
Virtual Instrumentation ◽  
Data Acquisition Board

This paper presents the development of a virtual didactic tool for students of mechatronic engineering taking an intelligent system course. The objective of the tool is for students to learn the structures for fuzzy control systems. This tool makes it easier for students to understand the behavior of the membership functions of input and output variables, the evaluation of the set of fuzzy rules, and the method of defuzzification, giving the students the possibility of applying a fuzzy controller in industrial processes using a data acquisition board. The proposed tool was developed with the virtual instrumentation software LabVIEW. It has the advantage that students can manipulate the internal structure of the fuzzy logic control system in a unique window where students can analyze the behavior of internal signals by looking at the response graphs. The fuzzy controller can be easily translated to a real application by using LabVIEW compatible hardware. To have feedback from students on the use of the tool and to understand if this tool allows an improvement in their academic performance, a 2-hour workshop on the proposed application was given to a group of 93 students. At the end of the workshop, a knowledge assessment and a perception survey were applied to the participants. The academic performance achieved by students who were given the workshop using the proposed teaching tool was compared with the academic performance of students who witnessed the workshop using Matlab tools. The statistical analysis of the results obtained for the knowledge assessment shows that the students that had taken the workshop using the proposed teaching tool had better compression of the topic compared to the students that had taken the workshop using the Fuzzy Logic Toolbox provided by Matlab MathWorks. The students that had taken the workshop using the proposed teaching tool obtained a mean grade of 89.63/100, while students that had taken the workshop using Matlab’s tools obtained a mean grade of 69.85/100. Also, the students’ perception of the proposed tool was that it allowed the design of fuzzy control systems in a simple and intuitive way.

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