Health Monitoring of a Drone Formation Affected by a Corrupted Control System

  • Nicolas Léchevin
  • Camille Alain Rabbath
  • Patrick Maupin
Reference work entry


Safe and reliable operation of formations unmanned aerial vehicles (UAVs) necessitates developing situational awareness capacities for assessing and predicting the health status of both network and assets, particularly when evolving in adversarial environments. This chapter proposes a dynamic feature that is instrumental in achieving vulnerability assessment of a network of UAVs, whose control system is possibly affected by the diffusion of malware. The feature consists of the characterization of the transition from stability to instability with probability one. The stability of the networked UAVs can be indirectly affected by malicious attacks targeting the communication units or the control systems. The network is modeled as a discrete-time, jump, linear system whose state- space variables represent the probabilities that each node receives a malware and is infected by it. The stability analysis is obtained by means of a stochastic Lyapunov function argument and yields a sufficient condition expressed as a linear matrix inequality (LMI). This LMI involves the networked asset state- space matrices and the probability that each UAV’s control system is infected. An approximation to the sufficient condition is proposed so that convergence of the system trajectories could be monitored online. The proposed detection technique is validated by simulations of a UAV formation.


Linear Matrix Inequality Unmanned Aerial Vehicle Malicious Code Formation Controller Jump Linear System 
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Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  1. 1.Defence Research and Development Canada – ValcartierQuébecCanada
  2. 2.Department of Mechanical and Industrial EngineeringConcordia UniversityMontrealCanada
  3. 3.Department of EngineeringConcordia UniversityMontrealCanada

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