Consensus Algorithms for Single-Integrator Multi-Agent Systems

Master thesis (2023)

Authors

E.P.M. Zwetsloot Electrical Engineering, Mathematics and Computer Science

Contributors

M. Mazo Team Manuel Mazo Jr - Mechanical, Maritime and Materials Engineering (supervisor 1)
R.T. Rajan Signal Processing Systems - EEMCS (supervisor 2)
D. Boskos Team Dimitris Boskos - Mechanical, Maritime and Materials Engineering (supervisor 2)
Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright: © 2023 Eva Zwetsloot
Consensus Algorithms Multi-agent systems Event triggered
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Published Date

25-05-2023

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

In multi-agent systems reaching consensus has been a long-standing problem. A considerable amount of research has been focused on how event-triggered consensus can be used to limit the energy consumption of the communication system while still ensuring convergence to the neighbourhood of a point or a formation. These algorithms rely on having knowledge of the global state information of each agent in the network. Given this information, it is possible to consider using distributed algorithms from the field of Computer Science to achieve the same.

This thesis presents a study on average consensus algorithms for single-integrator multi-agent systems. The focus is on comparing the time required to reach convergence and the energy required by the communication system. To perform this study, three methods are used to compare the algorithms. The first method involves deriving mathematical bounds on the convergence time and the number of transmissions. The second involves a simple simulation to verify the mathematical bounds. Finally, the algorithms are compared in a practical application where robots are subjected to noisy measurements and need to assemble in a formation.

The results of the comparisons show that distributed algorithms are capable of fast convergence and require less energy for communication. Therefore, it is worth considering the use of distributed algorithms when agents have enough memory to temporarily store the global state of all agents in the network. There might still be situations in which the ETC algorithm converges slightly faster but this benefit is overshadowed by the increase in energy consumption.