By Topic

Flocking of Multi-Agent Systems Via Model Predictive Control Based on Position-Only Measurements

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Jingyuan Zhan ; Adaptive Networks and Control Lab, Department of Electronic Engineering, Fudan University, Shanghai, China ; Xiang Li

The information of both the position and velocity of agents are required in most existing flocking algorithms. This paper studies the model predictive control (MPC) flocking of a networked multi-agent system based on position measurements only. We first propose a centralized impulsive MPC flocking algorithm and further develop a feasible sequential-negotiation based distributed impulsive MPC flocking algorithm, where each agent sequentially solves a local optimization control problem involving the states of its neighbors only. We prove that both the centralized and distributed impulsive MPC flocking algorithms lead to a stable flock by using geometric properties of the optimal path followed by individual agents and provide numerical simulation examples to illustrate their effectiveness and advantages in convergence rate and communication cost.

Published in:

IEEE Transactions on Industrial Informatics  (Volume:9 ,  Issue: 1 )