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High-Performance Control of Mean-Field Teams in Leader-Follower Networks

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High-Performance Control of Mean-Field Teams in Leader-Follower Networks

Baharloo, Mohammad Mahdi (2019) High-Performance Control of Mean-Field Teams in Leader-Follower Networks. Masters thesis, Concordia University.

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Abstract

In this thesis, a mean-field approach is used to find high-performance control strategies for multi-agent systems. The system consists of one leader and possibly many dynamically coupled followers, and all agents are affected by noise. The global objective of the multi-agent control system here is to achieve an agreement between the agents while minimizing coupled linear-quadratic cost functions for two cases: a disturbance-free system, and a system with disturbances. In the former case, the proposed solution under non-classical information structure is near-optimal, which converges to the optimal solution for a large number of followers. For the latter case, the problem is solved for three non-classical information structures, namely, mean-field sharing, partial mean-field sharing, and intermittent mean-field sharing. Using the minimax control technique, it is shown that the solution obtained for the first structure is a unique saddle-point strategy. On the other hand, it is proved that for the other two structures, the proposed solutions tend to the unique saddle-point strategy when the number of followers goes to infinity. The proposed strategies in both cases are linear, scalable and computationally efficient. The theoretical findings are verified by simulation results.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Electrical and Computer Engineering
Item Type:Thesis (Masters)
Authors:Baharloo, Mohammad Mahdi
Institution:Concordia University
Degree Name:M.A. Sc.
Program:Electrical and Computer Engineering
Date:29 March 2019
Thesis Supervisor(s):Aghdam, Amir G.
ID Code:985266
Deposited By: Mohammad Mahdi Baharloo
Deposited On:17 Jun 2019 19:29
Last Modified:17 Jun 2019 19:29
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