Research Papers

Flocking of Multi-Agent Systems Using a Unified Optimal Control Approach

[+] Author and Article Information
Jianan Wang

School of Electrical and Electronic Engineering,
University of Manchester,
Manchester M13 9PL, UK

Ming Xin

Department of Mechanical and
Aerospace Engineering,
University of Missouri,
Columbia, MO 65211
e-mail: xin618@gmail.com

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received October 28, 2011; final manuscript received June 25, 2013; published online August 23, 2013. Assoc. Editor: Marco P. Schoen.

J. Dyn. Sys., Meas., Control 135(6), 061005 (Aug 23, 2013) (11 pages) Paper No: DS-11-1338; doi: 10.1115/1.4024903 History: Received October 28, 2011; Revised June 25, 2013

In this paper, the multi-agent flocking problem is investigated in a unified optimal control framework. The flocking characteristics, such as velocity alignment, navigation, cohesion, and collision/obstacle avoidance, are accomplished by formulating them into respective cost function terms. The resultant nonquadratic cost function poses a challenging optimal control problem. A novel inverse optimal control strategy is adopted to derive an analytical optimal control law. The optimality and asymptotic stability are proved and the distributed feedback control law only requires local information to achieve the flocking behaviors. Various simulation scenarios are used to demonstrate the effectiveness of the optimal flocking algorithm.

Copyright © 2013 by ASME
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Fig. 1

Illustration of agents and obstacles

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Fig. 2

Communication topology and reference access

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Fig. 3

Scenario A: flocking demonstration with velocity alignment and navigation

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Fig. 4

Time histories of positions and velocities in scenario A

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Fig. 5

Scenario B: flocking demonstration with velocity alignment, navigation, and cohesion

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Fig. 6

Time histories of positions and velocities in scenario B

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Fig. 7

Scenario C: flocking demonstration with velocity alignment, navigation, cohesion, and obstacle/collision avoidance

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Fig. 8

Time histories of positions and velocities in scenario C

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Fig. 9

Time histories of control inputs in scenario C




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