scholarly journals Design of Connectivity Preserving Flocking Using Control Lyapunov Function

2016 ◽  
Vol 2016 ◽  
pp. 1-12
Author(s):  
Bayu Erfianto ◽  
Riyanto T. Bambang ◽  
Hilwadi Hindersah ◽  
Intan Muchtadi-Alamsyah
Keyword(s):  
Lyapunov Function ◽  
Potential Field ◽  
Control Design ◽  
Artificial Potential Field ◽  
Control Input ◽  
Control Lyapunov Function ◽  
Communication Topology ◽  
The Common ◽  
Flocking Motion

This paper investigates cooperative flocking control design with connectivity preserving mechanism. During flocking, interagent distance is measured to determine communication topology of the flocks. Then, cooperative flocking motion is built based on cooperative artificial potential field with connectivity preserving mechanism to achieve the common flocking objective. The flocking control input is then obtained by deriving cooperative artificial potential field using control Lyapunov function. As a result, we prove that our flocking protocol establishes group stabilization and the communication topology of multiagent flocking is always connected.

Backstepping Control Design on the Dynamics of the Omni-Directional Mobile Robot

2012 ◽  
Vol 203 ◽  
pp. 51-56 ◽  
Author(s):  
Qing Zhu Cui ◽  
Xun Li ◽  
Xiang Ke Wang ◽  
Meng Zhang
Keyword(s):  
Lyapunov Function ◽  
Mobile Robot ◽  
Global Stability ◽  
Model Simulation ◽  
Control Design ◽  
Dynamical Model ◽  
Control Input ◽  
Newtonian Mechanics ◽  
Virtual Control ◽  
Simulation Results

The dynamical model of an omni-directional mobile robot is bulit based on the Newtonian mechanics. Correspondingly, a backstepping-based controller is then proposed with proven global stability by selecting a Lyapunov function and introducing a virtual control input for the built dynamical model. Simulation results show the effectiveness of the proposed controller.

scholarly journals Fixed-Wing UAV Formation Control Design With Collision Avoidance Based on an Improved Artificial Potential Field

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 78342-78351 ◽  
Author(s):  
Jialong Zhang ◽  
Jianguo Yan ◽  
Pu Zhang
Keyword(s):  
Collision Avoidance ◽  
Potential Field ◽  
Formation Control ◽  
Control Design ◽  
Artificial Potential Field

scholarly journals Lithium-Ion Battery SoC Equilibrium: An Artificial Potential Field-Based Method

Energies ◽  
2020 ◽  
Vol 13 (21) ◽  
pp. 5691
Author(s):  
Hongtao Liao ◽  
Fu Jiang ◽  
Cheng Jin ◽  
Yue Wu ◽  
Heng Li ◽  
...  
Keyword(s):  
Lithium Ion Battery ◽  
Potential Field ◽  
Lithium Ion ◽  
Artificial Potential Field ◽  
Control Law ◽  
Efficient Operation ◽  
Virtual Force ◽  
Communication Topology ◽  
Charging Current ◽  
Circuit Components

Battery balance methods are the key technology to ensure the safe and efficient operation of the energy storage systems. Nevertheless, convenient balance methods experience slow convergence and difficult to adapt to quick charging applications. To solve the problem, in this paper, an artificial potential field-based lithium-ion battery balance method is proposed. Firstly, a cyber-physical model of the battery equalization system is proposed, in which the physical layer models the circuit components and the cyber layer represents the communication topology between the batteries. Then the virtual force function is established by artificial potential field to attract the voltage and state-of-charge of each cell to nominal values. With a feedback control law, the charging current of the battery is reasonably distributed to realize the rapid balance among batteries. The experimental results verify the effectiveness and superiority of the proposed method.

Attack and Defense Planning for Competitive Networked RobotsBased on Artificial Potential Field Method

ROBOT ◽  
2013 ◽  
Vol 35 (2) ◽  
pp. 218 ◽  
Author(s):  
Xiang LU ◽  
Jingtai LIU ◽  
Haifeng LI ◽  
Yan LI ◽  
Lei SUN
Keyword(s):  
Potential Field ◽  
Field Method ◽  
Artificial Potential Field ◽  
Potential Field Method ◽  
Attack And Defense ◽  
Artificial Potential Field Method ◽  
Defense Planning

scholarly journals Formation Control and Obstacle Avoidance Algorithm of a Multi-USV System Based on Virtual Structure and Artificial Potential Field

2021 ◽  
Vol 9 (2) ◽  
pp. 161
Author(s):  
Xun Yan ◽  
Dapeng Jiang ◽  
Runlong Miao ◽  
Yulong Li
Keyword(s):  
Obstacle Avoidance ◽  
Potential Field ◽  
Formation Control ◽  
Shape Change ◽  
Artificial Potential Field ◽  
Generation Algorithm ◽  
Time Performance ◽  
Movement Ability ◽  
Window Approach ◽  
Avoidance Problem

This paper proposes a formation generation algorithm and formation obstacle avoidance strategy for multiple unmanned surface vehicles (USVs). The proposed formation generation algorithm implements an approach combining a virtual structure and artificial potential field (VSAPF), which provides a high accuracy of formation shape keeping and flexibility of formation shape change. To solve the obstacle avoidance problem of the multi-USV system, an improved dynamic window approach is applied to the formation reference point, which considers the movement ability of the USV. By applying this method, the USV formation can avoid obstacles while maintaining its shape. The combination of the virtual structure and artificial potential field has the advantage of less calculations, so that it can ensure the real-time performance of the algorithm and convenience for deployment on an actual USV. Various simulation results for a group of USVs are provided to demonstrate the effectiveness of the proposed algorithms.

scholarly journals Research on path planning of three-neighbor search A* algorithm combined with artificial potential field

2021 ◽  
Vol 18 (3) ◽  
pp. 172988142110264
Author(s):  
Jiqing Chen ◽  
Chenzhi Tan ◽  
Rongxian Mo ◽  
Hongdu Zhang ◽  
Ganwei Cai ◽  
...  
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Obstacle Avoidance ◽  
Potential Field ◽  
Path Length ◽  
Search Time ◽  
Experimental Results ◽  
Artificial Potential Field ◽  
A Algorithm ◽  
Neighbor Search

Among the shortcomings of the A* algorithm, for example, there are many search nodes in path planning, and the calculation time is long. This article proposes a three-neighbor search A* algorithm combined with artificial potential fields to optimize the path planning problem of mobile robots. The algorithm integrates and improves the partial artificial potential field and the A* algorithm to address irregular obstacles in the forward direction. The artificial potential field guides the mobile robot to move forward quickly. The A* algorithm of the three-neighbor search method performs accurate obstacle avoidance. The current pose vector of the mobile robot is constructed during obstacle avoidance, the search range is narrowed to less than three neighbors, and repeated searches are avoided. In the matrix laboratory environment, grid maps with different obstacle ratios are compared with the A* algorithm. The experimental results show that the proposed improved algorithm avoids concave obstacle traps and shortens the path length, thus reducing the search time and the number of search nodes. The average path length is shortened by 5.58%, the path search time is shortened by 77.05%, and the number of path nodes is reduced by 88.85%. The experimental results fully show that the improved A* algorithm is effective and feasible and can provide optimal results.

Route planning of intelligent bridge cranes based on an improved artificial potential field method

2021 ◽  
pp. 1-8
Author(s):  
Zhengyan Chang ◽  
Zhengwei Zhang ◽  
Qiang Deng ◽  
Zheren Li
Keyword(s):  
Potential Field ◽  
Autonomous Navigation ◽  
Route Planning ◽  
Simple Algorithm ◽  
Field Method ◽  
Artificial Potential Field ◽  
Planning Problem ◽  
Operating Characteristics ◽  
Potential Field Method ◽  
Artificial Potential Field Method

The artificial potential field method is usually applied to the path planning problem of driverless cars or mobile robots. For example, it has been applied for the obstacle avoidance problem of intelligent cars and the autonomous navigation system of storage robots. However, there have been few studies on its application to intelligent bridge cranes. The artificial potential field method has the advantages of being a simple algorithm with short operation times. However, it is also prone to problems of unreachable targets and local minima. Based on the analysis of the operating characteristics of bridge cranes, a two-dimensional intelligent running environment model of a bridge crane was constructed in MATLAB. According to the basic theory of the artificial potential field method, the double-layer artificial potential field method was deduced, and the path and track fuzzy processing method was proposed. These two methods were implemented in MATLAB simulations. The results showed that the improved artificial potential field method could avoid static obstacles efficiently.

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