Adaptive Swarm Coordination and Formation Control

2020 ◽  
pp. 2041-2075
Author(s):  
Samet Guler ◽  
Baris Fidan ◽  
Veysel Gazi
Keyword(s):  
Adaptive Control ◽  
Graph Theory ◽  
Control Theory ◽  
Formation Control ◽  
The Other ◽  
System Behavior ◽  
Swarm Dynamics ◽  
Multi Agent ◽  
Control Designs ◽  
Swarm Coordination

Swarm coordination and formation control designs focus on multi-agent dynamic system behavior and aim to achieve desired coordinated behavior or predefined geometric shape. They utilize techniques from the control theory and graph theory literature. On the other hand, adaptive control theory is concerned with uncertainties in the system dynamics, and has structured frameworks for various types of plant models. Therefore, in case there are uncertainties in the swarm dynamics, adaptive control methodologies can be utilized to achieve the desired coordinated behavior and there exist remarkable works in this direction. However, connection among swarm coordination, formation control, and adaptive control theory brings some restrictions as well as advantages. Hence adaptive swarm coordination and formation control has been developed in limited aspects. In this chapter, we review some existing works of the adaptive formation control literature along with non-adaptive ones, and discuss the advantages of application of adaptive control frameworks to swarm coordination and formation control.

scholarly journals Multi-UAV Cooperative Target Tracking via Consensus-based Guidance Vector Fields and Fuzzy MRAC

2021 ◽  
Author(s):  
Tagir Muslimov ◽  
Rustem Munasypov
Keyword(s):  
Adaptive Control ◽  
Formation Control ◽  
Vector Fields ◽  
Fuzzy Model ◽  
Circular Path ◽  
Open Chain ◽  
Self Tuning ◽  
Multi Agent ◽  
And Performance ◽  
Model Reference Adaptive

This paper proposes a multi-agent approach to adaptive control of fixed-wing unmanned aerial vehicles (UAVs) tracking a moving ground target. The approach implies that the UAVs in a single group must maintain preset phase shift angles while rotating around the target so as to evaluate the target’s movement more ac-curately. Thus, the controls should ensure that: (1) the UAV swarm follows a moving circular path whose center is the target while also attaining and maintain-ing a circular formation of a specific geometric shape; (2) the formation control systems is capable of self-tuning since the UAV dynamics is uncertain. In con-trast to known studies, this one uses Lyapunov vector guidance fields that are di-rection- and magnitude-nonuniform. The overall cooperative controller structure is based on a decentralized and centralized consensus. This paper considers two interaction architectures: an open chain where each UAV only interacts with its neighbors; and cooperative leader, where the leading UAV is involved in attain-ing the formation. Using open chain decentralized architecture allows to have an unlimited number of aircraft in a group, which is in line with the swarm behavior concept. The cooperative controllers are self-tuned by fuzzy model reference adaptive control (MRAC). The approach was tested for efficiency and performance in various scenarios using complete nonlinear flying-wing UAV models equipped with configured standard autopilot models.<br>

scholarly journals Multi-UAV Cooperative Target Tracking via Consensus-based Guidance Vector Fields and Fuzzy MRAC

2021 ◽  
Author(s):  
Tagir Muslimov ◽  
Rustem Munasypov
Keyword(s):  
Adaptive Control ◽  
Formation Control ◽  
Vector Fields ◽  
Fuzzy Model ◽  
Circular Path ◽  
Open Chain ◽  
Self Tuning ◽  
Multi Agent ◽  
And Performance ◽  
Model Reference Adaptive

This paper proposes a multi-agent approach to adaptive control of fixed-wing unmanned aerial vehicles (UAVs) tracking a moving ground target. The approach implies that the UAVs in a single group must maintain preset phase shift angles while rotating around the target so as to evaluate the target’s movement more ac-curately. Thus, the controls should ensure that: (1) the UAV swarm follows a moving circular path whose center is the target while also attaining and maintain-ing a circular formation of a specific geometric shape; (2) the formation control systems is capable of self-tuning since the UAV dynamics is uncertain. In con-trast to known studies, this one uses Lyapunov vector guidance fields that are di-rection- and magnitude-nonuniform. The overall cooperative controller structure is based on a decentralized and centralized consensus. This paper considers two interaction architectures: an open chain where each UAV only interacts with its neighbors; and cooperative leader, where the leading UAV is involved in attain-ing the formation. Using open chain decentralized architecture allows to have an unlimited number of aircraft in a group, which is in line with the swarm behavior concept. The cooperative controllers are self-tuned by fuzzy model reference adaptive control (MRAC). The approach was tested for efficiency and performance in various scenarios using complete nonlinear flying-wing UAV models equipped with configured standard autopilot models.<br>

scholarly journals Design of the Multi-Agent Robot Group Formation Based on Pilot Followers

2016 ◽  
Vol 9 (1) ◽  
pp. 70
Author(s):  
Haiyang Zou
Keyword(s):  
Graph Theory ◽  
Control Strategy ◽  
Formation Control ◽  
Ant Colony Algorithm ◽  
Group Formation ◽  
Font Size ◽  
Structured Design ◽  
Multi Agent ◽  
Scale Design ◽  
Robot Group

<span style="font-size: 10.5pt; font-family: 'Times New Roman','serif'; mso-bidi-font-size: 10.0pt; mso-fareast-font-family: 宋体; mso-font-kerning: 1.0pt; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA;" lang="EN-US">Some animals always keep a certain formation in flight, it plays a good role for the defense of animal predators, avoid falling behind, avoid obstacles and other aspects. Based on the the design of pilot follows formation way, this topic from the characteristics of bionics, which makes the robot population to maintain a certain formation, they complete the relatively complex tasks through mutual coordination, division and cooperation.We adopt asymmetric formation control strategy and introduce the graph theory in the topic, it has good theoretical and practical guiding significance about the application of robot in real.In the formation of scale design, We adopt hypercube structured design concept, and ant colony algorithm of sub-cube for hypercube secondary division, multi-agent robot groups has better results in all aspects of the expansion, fault tolerance and transaction complexity.</span>

Dynamic Formation Control of Multi-Agent Systems Using Rigid Graphs

2014 ◽  
Author(s):  
Xiaoyu Cai ◽  
Marcio de Queiroz
Keyword(s):  
Graph Theory ◽  
Formation Control ◽  
Control Law ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Multi Agent ◽  
Simulation Results ◽  
Error Dynamics ◽  
Integrator Model ◽  
Dynamic Formation

In this paper, we consider the problem of formation control of multi-agent systems where the desired formation is dynamic. This is motivated by applications, such as obstacle avoidance, where the formation size and/or geometric shape needs to vary in time. Using a single-integrator model and rigid graph theory, we propose a new control law that exponentially stabilizes the origin of the nonlinear, inter-agent distance error dynamics and ensures tracking of the desired formation. The extension to the formation maneuvering problem is also discussed. Simulation results for a five-agent formation demonstrate the control in action.

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