Project Team Necs

Overall Objectives
Scientific Foundations
Application Domains
New Results
Contracts and Grants with Industry
Partnerships and Cooperations
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Section: New Results

Collaborative distributed consensus algorithms for control and estimation

Distributed Control

Participants : A. Seuret [Contact person] , C. Canudas de Wit, L. Briñón Arranz, G. Rodrigues de Campos, K. H. Johansson [KTH] .

The first contribution in this area deals with the source-seeking problem in which the task is to locate the source of some signal using a fleet of autonomous underwater vehicles. The objective is here to use the underwater vehicles equipped with appropriate sensors as a mobile sensors network. In [28] and [29] , we present a method which allows estimating the gradient of the signal propagation using a distributed consensus filters [27] . To do so, we consider a group of vehicles uniformly distributed in a fixed circular formation. We then show that this distributed consensus algorithm converges to good approximation of the gradient of the signal propagation. The algorithm takes into account the communication constraints and depends on direct signal measurements. Our approach is based on the previous results in formation control to stabilize the fleet to elastic formations which can be time-varying [29] and in a collaborative source-seeking algorithm proposed earlier by members of the team. The results are supported through computer simulations.

The second contribution on collaborative control concerns the design and analysis of a distributed algorithm whose goal is symmetric robot deployment. This activity results from the collaboration between INRIA and KTH provided by the visit of G. Rodrigues de Campos (PhD student) at KTH during six month. The objective is here to propose a hierarchical control strategy composed of two stages. The first one corresponds to an algorithm for swarm dispersion and a second concerns the design of a additional algorithm which minimizes the inter-agent angles. In this context, the behavior of each vehicle depends only on the relative positions of agents it can sense. The article submitted to ICRA'12 [84] , presents some simulation examples for different configuration support the derived theoretical results.

Distributed Estimation

Figure 5. Cooperative communication system.

Distributed Consensus

Distributed real-time Simulation of numerical models

Participants : D. Simon [Contact person] , A. Ben Khaled [IFPEN] , M. Ben Gaid [IFPEN] .

To allow real-time simulation of high fidelity engine models, different techniques have to be applied in order to fulfill the real-time constraints. Real-time simulation involves trade-offs between several aspects, such as real-time constraints, models computational complexity and integration accuracy. Traditionally HIL designers consider that every step of the simulation must be real-time and deterministic, leading to strongly synchronized systems, at the cost of ineffective computation burdens. It has been shown that adequately splitting the plant's model into weakly synchronized sub-systems allows for efficiently using variable steps numerical integrators, simulation speed-ups and subsequent effective parallel versions of HIL systems [25] .