CRAN - Campus Sciences
BP 70239 - 54506 VANDOEUVRE Cedex
Tél : +33 (0)3 72 74 52 90 Fax : +33 (0)3 72 74 53 08
cran-secretariat@univ-lorraine.fr
 
 
Sujet de Thèse : Conception de méthodes de diagnostic et de tolérance aux fautes des systèmes multi-agents: Application à une flotte de véhicules autonomes
Dates : 2014/10/01 - 2017/09/30
Directeur(s) CRAN : Didier THEILLIOL
Autre(s) Directeur(s) : Dr CIARLETTA Laurent (LORIA) (laurent.ciarletta@loria.fr)
Description : Distributed coordination of multiple networked dynamic agents has spurred a broad interest in the last decade. To execute a common mission using networked multi-agent systems, the consistent agreement, called “consensus”, is required. The agreement variables are rendered with specific physical quantities in different environments, such as the workload in a network of parallel computers and the clock speed for wireless sensor networks. The “consensus algorithm” is a common iteration rule, which specifies the information exchange relationship and the instantaneous state update law for each single agent. To enable the capability of accomplishing a common work cooperatively, information consensus is crucial. The information consensus is investigated in diverse fields due to its broad potential applications. The social animals are behaved in group movement naturally, i.e. fishes schooling, birds flocking and herds of buffaloes, etc. This phenomenon is studied using the boid model in [1]. Ref. [2] further formalized the flocking for multi-agent dynamic systems. Since all the agents are coupled via network and no centralized controller can monitor the entire system, it is highly possible that the team objective will be crushed if one agent stops functioning normally. Unlike the centralized faulty system, the non-functional agent in distributed system is probably unobservable by the agents out of its neighbourhood. Hence, fault detection and isolation (FDI) problem is more challenging in multi-agent system. To deal with the faulty agents in distributed multi-agent system, Ref. [3] developed a distributed function calculation method with a broadcast model. Each agent updates its state periodically as a weighted linear combination of its own state and the neighbours’. Since the weights of a consensus algorithm are determined by the network structure, its fault-tolerant capability to a specific malicious behaviour is decided by the communication topology. With the help of motion probe, Ref. [4] discussed a way of detecting faulty agent with single integrator dynamics. In addition to their work, Ref. [5] took the investigation further on active fault diagnosis and identification, which was an application of the motion probe and proposed a formal classification for agent faults. Unlike the classification in Ref. [5], two kinds of misbehaving agents are categorized mathematically in Ref. [6]: non-colluding (or faulty) and Byzantine (malicious) agent. As for the non-colluding agents, their malfunctions are purely caused by random faults. If an agent with the purpose of destroying the group mission disseminates the intriguing messages, this agent is denoted as the Byzantine agent. Other than the fault detection strategies based on ideal model, the influence caused by unknown input is investigated in [7]. To mitigate the computational workload for each agent, Ref. [8] conducts a real-time distributed fault detection strategy. The robustness is also considered in the proposed fault isolation procedure.
Based on the previous work, the main objective of our research is to systematically develop fault detection and isolation methods and the corresponding fault tolerant (FT) method. Unlike the classical FDI/FT problem, there is no centralized controller handling global information. The nodes acquire only local knowledge in networked multi-agent system (NMAS). Although recently some heuristic attempts are paving the approach to FDI/FT in NMAS, the fault diagnosis techniques in NMAS are still challenging. The following research objectives will be achieved through this research:
(1). Development of an effective residual generator for distributed multi-agent system;
(2). Development of an unknown input decoupling strategy to better distinguishes the candidate faults;
(3). Development of a systematic methodology for FDI/FT problem in distributed multi-agent system;
(4). Application of the developed FDI/FT methods to multi-agent systems to a multi-UAVs (Unmanned Aerial Vehicles) test bed [9][10].
The solution developed in this program will first be evaluated in pure simulation with a final goal of being embedded in real systems. The challenge of real-time communications in the overall system should be solved at the platform level.

[1] Reynolds, C., “Flocks, herds and schools: a distributed behavioral model,” Proceedings of ACM SIGGRAPH Conference, 1987.
[2] Olfati-Saber, R., “Flocking for Multi-Agent Dynamic Systems: Algorithms and Theory,” IEEE Transactions on Automatic Control, Vol. 51, No. 3, 2006, pp. 401-20.
[3] Sundaram, S. and Hadjicostis, C. N., “Distributed Function Calculation via Linear Iterative Strategies in the Presence of Malicious Agents,” IEEE Transactions on Automatic Control, Vol. 56, No. 7, 2011, pp. 1495-1508.
[4] Franceschelli, M., Egerstedt, M., and Giua, A., “Motion probes for fault detection and recovery in networked control systems,” Proceedings of the 2008 American Control Conference, 2008, pp. 4358-63.
[5] Franceschelli, M., Giua, A., and Seatzu, C., “Decentralized fault diagnosis for sensor networks,” Proceedings of the 2009 IEEE International Conference on Automation Science and Engineering, 2009, pp. 334-339.
[6] Pasqualetti, F., Bicchi, A., and Bullo, F., “Consensus Computation in Unreliable Networks: A System Theoretic Approach,” IEEE Transactions on Automatic Control , Vol. 57, No. 1, 2012, pp. 90-104.
[7] Shames, I., Teixeira, A. M., Sandberg, H., and Johansson, K. H., “Distributed fault detection for interconnected second-order systems,” Automatica, Vol. 47, No. 12, 2011, pp. 2757-64.
[8] Guo, M., Dimarogonas, D. V., and Johansson, K. H., “Distributed real-time fault detection and isolation for cooperative multi-agent systems,” Proceedings of American Control Conference, 2012, pp. 5270-75.
[9] Ciarletta L., and Guénard A. , “The AETOURNOS project: Using a flock of UAVs as a Cyber Physical System and platform for application-driven research”, EmSens - 2nd International Workshop on Emerging Topics on Sensor Networks - 2012 10 (2012) pp. 939-945
[10] Siebert J., Ciarletta L., and Chevrier V, “Agents and Artefacts for Multiple Models coordination. Objective and decentralized coordination of simulators”, 25th Symposium on Applied Computing - SAC 2010 (2010) pp. 2024-2028.

Mots clés : FDI/FT methods Design to multi-agent systems: Application to Fleet Autonomous Vehicles
Département(s) :
Contrôle Identification Diagnostic
Financement : Contrat doctoral UL 50% Fédération Charles Hermite, 50% Région Lorraine