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.

UNMANNED VEHICLE CONTROL SYSTEM BASED ON FUZZY CLUSTERING. PART 2. FUZZY CLUSTERING AND SOFTWARE IMPLEMENTATION

2020 ◽  
pp. 21-29
Author(s):  
A. S. Akopov ◽  
N. K. Khachatryan ◽  
L. A. Beklaryan ◽  
A. L. Beklaryan
Keyword(s):  
Control System ◽  
Fuzzy Clustering ◽  
Numerical Experiments ◽  
Heavy Traffic ◽  
Unmanned Vehicles ◽  
Software Implementation ◽  
Total Output ◽  
Clustering Methods ◽  
The Road ◽  
Fuzzy Clustering Methods

This article continues the description of the control system for ground unmanned vehicles as part of the integration of a phenomenological approach to modeling the behavior of agents and methods of fuzzy clustering in order to improve the quality of decisions. As a result, adaptive fuzzy clustering methods provide support for adaptive ground unmanned vehicles control, which minimizes the risks of accidents (emergencies involving ground unmanned vehicles) and maximizes traffic (total output stream) in conditions of heavy traffic. The second part is devoted to the description of the developed fuzzy clustering algorithm, software implementation and experiments. As a result, within the framework of the developed model of ground unmanned vehicles movement, fuzzy clustering methods are used to ensure the procedure for choosing the most preferable (least dense) lane in conditions of heavy traffic and to support continuous information exchange between ground unmanned vehicles. The software implementation of the developed simulation model in the AnyLogic environment was performed and numerical experiments aimed at analyzing scenarios of the development of the road situation with the participation of the ground unmanned vehicles ensemble were carried out. Various behavioral scenarios of the developed ground unmanned vehicles control system were investigated, and agent clustering was performed for each scenario under consideration. As a result of numerical experiments, the effectiveness of using the proposed fuzzy clustering procedure to assess the density of the road flow and adaptive control and maneuvering of the ground unmanned vehicles is confirmed.

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.

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.

scholarly journals Surface Roughness Image Analysis using Quasi-Fractal Characteristics and Fuzzy Clustering Methods

2008 ◽  
Vol 3 (3) ◽  
pp. 304 ◽  
Author(s):  
Tiberiu Vesselenyi ◽  
Ioan Dzitac ◽  
Simona Dzitac ◽  
Victor Vaida
Keyword(s):  
Surface Roughness ◽  
Image Analysis ◽  
Control System ◽  
Fuzzy Clustering ◽  
Image Acquisition ◽  
Roughness Parameter ◽  
Clustering Methods ◽  
Fuzzy Clustering Methods ◽  
Fractal Characteristics ◽  
Roughness Control

In this paper the authors describe the results of experiments for surface roughness image acquisition and processing in order to develop an automated roughness control system. This implies the finding of a characteristic roughness parameter (for example Ra) on the bases of information contained in the image of the surface. To achieve this goal we use quasi-fractal characteristics and fuzzy clustering methods.

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.

Development of Mathematical Model of Ground Unmanned Vehicle Movement

2019 ◽  
Vol 15 (2) ◽  
pp. 618-625
Author(s):  
Nikulin Artem Anatolyevich ◽  
Bychkov Dmitriy Sergeevich ◽  
Generalova Alexandra Alexandrovna
Keyword(s):  
Mathematical Model ◽  
Unmanned Vehicle ◽  
Vehicle Movement

scholarly journals Fuzzy Clustering Methods with Rényi Relative Entropy and Cluster Size

Mathematics ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 1423
Author(s):  
Javier Bonilla ◽  
Daniel Vélez ◽  
Javier Montero ◽  
J. Tinguaro Rodríguez
Keyword(s):  
Relative Entropy ◽  
Fuzzy Clustering ◽  
Cluster Size ◽  
Computational Study ◽  
Gaussian Kernel ◽  
Clustering Methods ◽  
Rényi Divergence ◽  
Divergence Measures ◽  
Fuzzy Clustering Methods ◽  
Rényi Relative Entropy

In the last two decades, information entropy measures have been relevantly applied in fuzzy clustering problems in order to regularize solutions by avoiding the formation of partitions with excessively overlapping clusters. Following this idea, relative entropy or divergence measures have been similarly applied, particularly to enable that kind of entropy-based regularization to also take into account, as well as interact with, cluster size variables. Particularly, since Rényi divergence generalizes several other divergence measures, its application in fuzzy clustering seems promising for devising more general and potentially more effective methods. However, previous works making use of either Rényi entropy or divergence in fuzzy clustering, respectively, have not considered cluster sizes (thus applying regularization in terms of entropy, not divergence) or employed divergence without a regularization purpose. Then, the main contribution of this work is the introduction of a new regularization term based on Rényi relative entropy between membership degrees and observation ratios per cluster to penalize overlapping solutions in fuzzy clustering analysis. Specifically, such Rényi divergence-based term is added to the variance-based Fuzzy C-means objective function when allowing cluster sizes. This then leads to the development of two new fuzzy clustering methods exhibiting Rényi divergence-based regularization, the second one extending the first by considering a Gaussian kernel metric instead of the Euclidean distance. Iterative expressions for these methods are derived through the explicit application of Lagrange multipliers. An interesting feature of these expressions is that the proposed methods seem to take advantage of a greater amount of information in the updating steps for membership degrees and observations ratios per cluster. Finally, an extensive computational study is presented showing the feasibility and comparatively good performance of the proposed methods.

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