scholarly journals Optimized formation control of multi-agent system using PSO algorithm

2020 ◽  
Vol 20 (3) ◽  
pp. 1591
Author(s):  
Ahmed Mudheher Hasan ◽  
Safanah Mudheher Raafat
Keyword(s):  
Formation Control ◽  
Pso Algorithm ◽  
Network Control ◽  
Consensus Algorithm ◽  
Geometrical Shape ◽  
Multi Agent Systems ◽  
Mobile Vehicle ◽  
Aerial Vehicle ◽  
Multi Agent ◽  
Formation Keeping

<p>Formation Control (FC) is an important application for Multi-agent Systems (MASs) in coordinated control and especially for Unmanned Aerial Vehicle (UAV) which are widely used nowadays in military and civil sections. FC is mostly applied in conjunction with consensus algorithm. In this paper, a framework for an implementation of consensus FC that involves the decentralized type of network control is considered in order to achieve formation keeping, where the control of each vehicle is calculated dependent upon locally existed facts. The dynamic behavior of each vehicle agent is governed by its second-order dynamic model, and the networked mobile vehicle system is modeled by a directed graph. Then, Particle Swarm Optimization (PSO) is implemented for speeding up the convergence to the desired geometrical shape. Acceleration of the network while approaching the coveted shape is achieved and omissions of undesired swing that transpires through the acceleration is examined. The merits and effectiveness of the applied approach are demonstrated using two different examples.</p>

Energy-Balanced Leader-Switching Policy for Formation Rotation Control of Multi-Agent Systems Inspired by Bird Flocks

2019 ◽  
Author(s):  
Corey Dotson ◽  
Geronimo Macias ◽  
Kooktae Lee
Keyword(s):  
Formation Control ◽  
Consensus Algorithm ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Switching Algorithm ◽  
Multi Agent ◽  
Migrating Birds ◽  
Switching Method ◽  
Rotation Strategy ◽  
Flocking Behavior

Abstract This paper addresses an energy-balanced leader-switching policy for formation rotation control of multi-agent systems inspired by bird flocks. Birds that flock in V-formation with a leader rotation strategy are able to travel longer distances due to reduced drag and therefore less energy expenditure. This flocking behavior with a leader rotation will result in more conservation of overall energy and will be particularly beneficial to migrating birds that should fly long distances without landing. In this paper, we propose an energy-balanced leader-switching policy inspired by this bird flocking behavior in order to increase the flight range for multi-agent systems. The formation control of multi-agent systems is achieved by the consensus algorithm, which is fully decentralized through the use of information exchanges between agents. The proposed leader-switching method is not necessarily incorporated with the consensus dynamics and thus, the leader-switching algorithm can be decoupled from formation control dynamics. Therefore, the proposed method can simplify the leader-switching algorithm, making it easy to implement. Moreover, we propose the analytic flight distance based on the energy consumption model for each agent. To test the validity of the developed method, several simulation results are presented.

scholarly journals H∞Formation Control and Obstacle Avoidance for Hybrid Multi-Agent Systems

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Dong Xue ◽  
Jing Yao ◽  
Jun Wang
Keyword(s):  
Obstacle Avoidance ◽  
Formation Control ◽  
Multiagent System ◽  
Newtonian Potential ◽  
Planning Problem ◽  
Multi Agent Systems ◽  
Attenuation Level ◽  
Negative Effect ◽  
Multi Agent ◽  
Formation Keeping

A new concept ofH∞formation is proposed to handle a group of agents navigating in a free and an obstacle-laden environment while maintaining a desired formation and changing formations when required. With respect to the requirements of changing formation subject to internal or external events, a hybrid multiagent system (HMAS) is formulated in this paper. Based on the fact that obstacles impose the negative effect on the formation of HMAS, theH∞formation is introduced to reflect the above disturbed situation and quantify the attenuation level of obstacle avoidance via theH∞-norm of formation stability. An improved Newtonian potential function and a set of repulsive functions are employed to guarantee the HMAS formation-keeping and collision-avoiding from obstacles in a path planning problem, respectively. Simulation results in this paper show that the proposed formation algorithms can effectively allow the multiagent system to avoid penetration into obstacles while accomplishing prespecified global objective successfully.

A Geometric Approach to Dynamically Feasible, Real-Time Formation Control

2011 ◽  
Vol 133 (2) ◽  
Author(s):  
D. H. A. Maithripala ◽  
D. H. S. Maithripala ◽  
S. Jayasuriya
Keyword(s):  
Real Time ◽  
Wide Class ◽  
Formation Control ◽  
Geometric Approach ◽  
Control Problems ◽  
Multi Agent Systems ◽  
Individual Agent ◽  
Planning Algorithm ◽  
Explicit Consideration ◽  
Multi Agent

We propose a framework for synthesizing real-time trajectories for a wide class of coordinating multi-agent systems. The class of problems considered is characterized by the ability to decompose a given formation objective into an equivalent set of lower dimensional problems. These include the so called radar deception problem and the formation control problems that fall under formation keeping and/or formation reconfiguration tasks. The decomposition makes the approach scalable, computationally economical, and decentralized. Most importantly, the designed trajectories are dynamically feasible, meaning that they maintain the formation while satisfying the nonholonomic and saturation type velocity and acceleration constraints of each individual agent. The main contributions of this paper are (i) explicit consideration of second order dynamics for agents, (ii) explicit consideration of nonholonomic and saturation type velocity and acceleration constraints, (iii) unification of a wide class of formation control problems, and (iv) development of a real-time, distributed, scalable, computationally economical motion planning algorithm.

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