Defense of Pablo De Villeros

With the development of distributed computing and large-scale networks over the past decade, consensus-based distributed optimization has become a very active topic in control engineering due to its application in resource allocation, smart grids, sensor networks and machine learning. Furthermore, in some motion coordination applications, several physical agents need to establish a specific formation around an optimal location for the whole fleet. This is a more general case of consensus, where the final position of each agent is offset from a desired configuration.

Applications of formation control can be found in the automotive and aerospace fields, ranging from
structure assembly to exploration of unknown environments, navigation in hostile environments and cooperative transportation. However, most distributed optimization algorithms do not always exhibit a reasonable rate of convergence.

This thesis aims to design new distributed optimization algorithms using a multi-agent system formalism (i.e., a consensus-based approach) while considering time as a strong constraint. In particular, the algorithms in this work are developed within the framework of fixed-time convergence with an arbitrary upper bound on the settling time, and have been proven to be robust to inter-agent communication problems and external perturbations. Several simulations are performed to compare the proposed algorithms with existing schemes and evaluate their applicability to source estimation and collaborative learning.

• Mr. Constantin Morarescu. PR. U. de LORRAINE, France.
• Mr. Emmanuel Nuño Ortega. PR. UDG, Mexico
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• Mrs. Federica Garin. CR. INRIA, , France.
• Mr. Jorge Rivera Domínguez. PR. CINVESTAV, Mexico.
• Mr José Javier Ruiz León. PR. CINVESTAV, Mexico.

• Mr. Michael Defoort. PR UPHF, France.
• Mr Alexandre Loukianov. PR. CINVESTAV, Mexico.

• Mr. Mohamed Djemai. PR. UPHF, France.