Intelligent Detection Method for Roll Stability of Unmanned Vehicle based on Fuzzy Control

2021 ◽  
Vol 13 (4) ◽  
Author(s):  
Jun Chen
Keyword(s):  
Control System ◽  
Fuzzy Control ◽  
Detection Method ◽  
Lower Layer ◽  
Unmanned Vehicles ◽  
Roll Angle ◽  
Detection Accuracy ◽  
Unmanned Vehicle ◽  
Roll Control ◽  
Intelligent Detection

When the unmanned vehicle is disturbed by the outside world or carries out dangerous actions such as steering and continuous lane changing, the yaw stability of the unmanned vehicle decreases and the dangerous situation such as rollover is easy to occur. In this paper, the intelligent detection method for roll stability of unmanned vehicles based on fuzzy control is studied. The roll control system of the unmanned vehicle based on a double-layer control strategy is designed. The roll stability of the unmanned vehicle is controlled by an upper-layer fuzzy controller and lower-layer differential braking control. The dynamic model and tire model are built in MATLAB/Simulink to restore the running characteristics of unmanned vehicles. Based on the operation characteristics, the roll stability of the unmanned vehicle’s roll control system based on fuzzy control is tested from three aspects: steady-state response, roll stability and dynamic stability coefficient. The experimental results show that the transverse load’s transfer rate of the proposed method is reduced by more than 0.2% compared with the contrast method, the yaw angular velocity, centroid’s roll angle and roll angle measured under the two working conditions are closer to the actual values, which shows that the method has better control effect and detection accuracy.

UNMANNED VEHICLE CONTROL SYSTEM BASED ON FUZZY CLUSTERING. PART 1. VEHICLE MOVEMENT MODEL

2020 ◽  
pp. 3-12
Author(s):  
A. S. Akopov ◽  
N. K. Khachatryan ◽  
L. A. Beklaryan ◽  
A. L. Beklaryan
Keyword(s):  
Control System ◽  
Fuzzy Clustering ◽  
Personal Space ◽  
Computational Procedure ◽  
Unmanned Vehicles ◽  
Final Decision ◽  
Clustering Methods ◽  
Movement Model ◽  
Unmanned Vehicle ◽  
Vehicle Movement

A control system for ground unmanned vehicles is presented, using fuzzy clustering methods for making decisions at an individual level. A new approach to the management of ground unmanned vehicles has been developed, taking into account the state of vehicles in a dense traffic, in particular, the presence of road accidents, the appearance of traffic congestion (high density clusters), etc. An important advantage of this approach is the description of the rules for the interaction of various agents with each other and the external environment within the framework of the final decision-making system of individual agents without the need for a complex computational procedure for identifying the potentials of various forces of the system as a whole. In particular, such rules can be described using systems of differential equations with a variable structure, taking into account all the variety of possible interactions and collisions (potential collisions) between different (moving or stationary) objects. A key feature of the proposed model is the use of the concept of the radius of the agent’s personal space, which explains the effects of turbulence and crush. In this case, the radius of the agent’s personal space is a function of the density of vehicles. As a result, a model of unmanned vehicle movement is developed.

Fuzzy Logic Controller Designing of an Active Roll Control System for Medium and Large Size Vehicles

2011 ◽  
Vol 110-116 ◽  
pp. 4845-4855 ◽  
Author(s):  
Peiman Hajishafieiha
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Fuzzy Logic Controller ◽  
Roll Angle ◽  
Torsional Stiffness ◽  
Roll Control ◽  
Large Size ◽  
Handling Characteristics ◽  
Active Roll Control ◽  
Roll Axis

The roll / ride trade-off is a long-standing challenge to vehicle dynamicists. Achieving better ride performance almost inevitably leads to increased roll of the vehicle. This roll motion, mostly induced by maneuvering, leads to undesirable handling characteristics and subsequently higher risk of rollover. This paper analyses the use of an Active Roll Control (ARC) system with a Fuzzy Logic Controller (FLC) for improving the handling without sacrificing the ride comfort. The logic for reducing the roll angle of the vehicle is to have some forces exerted by linear actuators on the suspension system, depending on the velocity and steering angle of the vehicle. These forces create a moment about the roll axis which decreases the roll angle. The proposed Fuzzy logic controller is a feedback controller which outputs the correcting roll moment about the roll axis. The effects of employing such a control system are evaluated through computer simulation. Torsional stiffness of the chassis is then taken into consideration to account for unique properties of large-size vehicles. Simulation results with Fuzzy logic controller are very promising and show that the roll performance is significantly improved compared to the vehicle without ARC.

scholarly journals CONTROL SYSTEM STRUCTURE AND LOGIC FOR UNMANNED VEHICLES

2021 ◽  
Vol 23 (4) ◽  
pp. 96-102
Author(s):  
D.E. Chickrin ◽  
Keyword(s):  
Control System ◽  
Control Unit ◽  
Vehicle Control ◽  
Unmanned Vehicles ◽  
System Structure ◽  
Unmanned Vehicle ◽  
Control Subsystem

The article presents the author developed requirements for the key subsystem of the unmanned vehicle: the control subsystem, with the details of the unmanned vehicle control modes; proposals are given for the implementation of a three-level unmanned vehicle control unit, according to the concept of a "modular crate" proposed by the author.

Design about Automatic Vehicles Following System for Field Vibrator Motorcade

2013 ◽  
Vol 860-863 ◽  
pp. 2654-2659
Author(s):  
Shu Qing Li ◽  
Huan Zhang ◽  
Zhi Fei Tao
Keyword(s):  
Control System ◽  
Low Cost ◽  
Unmanned Vehicles ◽  
Good Effect ◽  
Unmanned Vehicle ◽  
Distance Control ◽  
Key Points ◽  
Vehicle Platoon ◽  
Real Time Information ◽  
And Control

As is known that the key points of unmanned vehicles are environmental perception, path planning and vehicles control. In order to achieve the goal of unmanned operations of the vibrator motorcade in the field, a low-cost automatic vehicles following system was established, which provided with environmental monitoring, real-time information feedback and control. And the designed system simplified the algorithm on the premise of ensuring the control accuracy and had a good effect on vehicles distance control with the help of Visual Studio. Finally, the LabVIEW program was programmed to guide the practical vibrator motorcade test, implementing the requirements of unmanned vehicle platoon control system. The results show that the designed vehicles distance control system works well in security distance control and meets the needs of vibrator motorcade, providing feasible reference for future specific applications of unmanned vehicle platoon in practice.

Research on Intelligent Variable Frequency of Air-Conditioner Control System Based on Fuzzy Control

2011 ◽  
Vol 110-116 ◽  
pp. 4076-4084
Author(s):  
Hai Cun Du
Keyword(s):  
Neural Network ◽  
Control System ◽  
Fuzzy Control ◽  
Fuzzy Neural Network ◽  
Air Conditioner ◽  
Neural Network Controller ◽  
The Neural Network ◽  
Network Controller ◽  
Fuzzy Neural ◽  
Fuzzy Control Strategy

In this paper, we determine the fuzzy control strategy of inverter air conditioner, the fuzzy control model structure, the neural network and fuzzy control technology, structural design of the fuzzy neural network controller as well as the neural network predictor FNNC NNP. Simulation results show that the fuzzy neural network controller can control the accuracy greatly improved the compressor, and the control system has strong adaptability to achieve a truly intelligent; model of the controller design and implementation of technology are mainly from the practical point of view, which is practical and feasible.

Design and Implementation of a Fuzzy Control System Applied to a 6 × 4 SRG

2021 ◽  
Vol 57 (1) ◽  
pp. 528-536
Author(s):  
Ghunter Paulo Viajante ◽  
Eric Nery Chaves ◽  
Luis Carlos Miranda ◽  
Marcos Antonio A. de Freitas ◽  
Carlos Antunes de Queiroz ◽  
...  
Keyword(s):  
Control System ◽  
Fuzzy Control ◽  
Fuzzy Control System ◽  
Design And Implementation

scholarly journals An Automatic Concrete Crack-Detection Method Fusing Point Clouds and Images Based on Improved Otsu’s Algorithm

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1581
Author(s):  
Xiaolong Chen ◽  
Jian Li ◽  
Shuowen Huang ◽  
Hao Cui ◽  
Peirong Liu ◽  
...  
Keyword(s):  
Crack Detection ◽  
Detection Method ◽  
Multiple Scales ◽  
Point Clouds ◽  
Depth Image ◽  
Detection Accuracy ◽  
Detection Techniques ◽  
Concrete Crack ◽  
3D Point Clouds ◽  
2D Images

Cracks are one of the main distresses that occur on concrete surfaces. Traditional methods for detecting cracks based on two-dimensional (2D) images can be hampered by stains, shadows, and other artifacts, while various three-dimensional (3D) crack-detection techniques, using point clouds, are less affected in this regard but are limited by the measurement accuracy of the 3D laser scanner. In this study, we propose an automatic crack-detection method that fuses 3D point clouds and 2D images based on an improved Otsu algorithm, which consists of the following four major procedures. First, a high-precision registration of a depth image projected from 3D point clouds and 2D images is performed. Second, pixel-level image fusion is performed, which fuses the depth and gray information. Third, a rough crack image is obtained from the fusion image using the improved Otsu method. Finally, the connected domain labeling and morphological methods are used to finely extract the cracks. Experimentally, the proposed method was tested at multiple scales and with various types of concrete crack. The results demonstrate that the proposed method can achieve an average precision of 89.0%, recall of 84.8%, and F1 score of 86.7%, performing significantly better than the single image (average F1 score of 67.6%) and single point cloud (average F1 score of 76.0%) methods. Accordingly, the proposed method has high detection accuracy and universality, indicating its wide potential application as an automatic method for concrete-crack detection.

scholarly journals Examination of Abnormal Behavior Detection Based on Improved YOLOv3

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 197
Author(s):  
Meng-ting Fang ◽  
Zhong-ju Chen ◽  
Krzysztof Przystupa ◽  
Tao Li ◽  
Michal Majka ◽  
...  
Keyword(s):  
Detection Method ◽  
Abnormal Behavior ◽  
Detection Accuracy ◽  
Average Precision ◽  
Research Results ◽  
Behavior Detection ◽  
Abnormal Behavior Detection ◽  
The Mean ◽  
Examination Room ◽  
Detection Speed

Examination is a way to select talents, and a perfect invigilation strategy can improve the fairness of the examination. To realize the automatic detection of abnormal behavior in the examination room, the method based on the improved YOLOv3 (The third version of the You Only Look Once algorithm) algorithm is proposed. The YOLOv3 algorithm is improved by using the K-Means algorithm, GIoUloss, focal loss, and Darknet32. In addition, the frame-alternate dual-thread method is used to optimize the detection process. The research results show that the improved YOLOv3 algorithm can improve both the detection accuracy and detection speed. The frame-alternate dual-thread method can greatly increase the detection speed. The mean Average Precision (mAP) of the improved YOLOv3 algorithm on the test set reached 88.53%, and the detection speed reached 42 Frames Per Second (FPS) in the frame-alternate dual-thread detection method. The research results provide a certain reference for automated invigilation.

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