Towards Decentralized Fault Detection in UAV Formations

Léchevin, N.; Rabbath, C.A.; Earon, Ernest J. P.

doi:10.1109/acc.2007.4282194

Cited by 17 publications

(7 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…., N . With and , applying Barbalat Lemma (20) , we conclude that lim t → ∞ e i ( t ) = 0 for i = 1, . .…”

Section: Adaptive Formation Flight Control Designmentioning

confidence: 94%

“…Therefore, the distributed control strategy relies on a relatively simple control system. For the decentralised control strategy, each UAV only needs to keep itself and the fixed points with the relative relations in the formation and does not communicate with others (20) . As there is almost no information interactions among UAVs, it greatly decreases the computing load and depends on the simplest structure.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multivariable adaptive distributed leader-follower flight control for multiple UAVs formation

Zhen

2017

Aeronaut. j.

View full text Add to dashboard Cite

The Unmanned Aerial Vehicles (UAVs) become more and more popular due to various potential application fields. This paper studies the distributed leader-follower formation flight control problem of multiple UAVs with uncertain parameters for both the leader and followers. This problem has not been addressed in the literature. Most of the existing literature considers the leader-follower formation control strategy with parametric uncertainty for the followers. However, they do not take the leader parametric uncertainty into account. Meanwhile, the distributed control strategy depends on less information interactions and is more likely to avoid information conflict. The dynamic model of the UAVs is established based on the aerodynamic parameters. The establishment of the topology structure between a collection of UAVs is based on the algebraic graph theory. To handle the parametric uncertainty of the UAVs dynamics, a multivariable model reference adaptive control (MRAC) method is addressed to design the control law, which enables follower UAVs to track the leader UAV. The stability of the formation flight control system is proved by the Lyapunov theory. Simulation results show that the proposed distributed adaptive leader-following formation flight control system has stronger robustness and adaptivity than the fixed control system, as well as the existing adaptive control system.

show abstract

“…., N . With and , applying Barbalat Lemma (20) , we conclude that lim t → ∞ e i ( t ) = 0 for i = 1, . .…”

Section: Adaptive Formation Flight Control Designmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

Multivariable adaptive distributed leader-follower flight control for multiple UAVs formation

Zhen

2017

Aeronaut. j.

View full text Add to dashboard Cite

show abstract

“…Furthermore, analysis of fault scenarios and effects in distributed systems were presented in [107,45]. But, as far as distributed discrete-time or continuous-time systems are concerned, only qualitative fault diagnosis schemes were attempted very recently [70,93,21,55], or quantitative methods that were formulated for linear systems only [18,56,59,102].…”

Section: Motivationmentioning

confidence: 99%

Distributed Fault Detection and Isolation of Continuous-Time Non-Linear Systems

Boem

Ferrari

Parisini

2011

European Journal of Control

View full text Add to dashboard Cite

“…In this architecture, all nodes are equipped with fault diagnosis algorithm. Fault diagnosis for a node is achieved in a collaborative way and the results of the algorithms in the neighbors of the monitored node are all needed [15,16]. It is worth noting that distributed architecture is much more reliable than the centralized one and the hierarchical one.…”

Section: Introductionmentioning

confidence: 99%

Method for Unit Self-Diagnosis at System Level

Mashkov¹,

Lytvynenko²

2019

IJISA

View full text Add to dashboard Cite

This paper suggests unconventional approach to system level self-diagnosis. Traditionally, system level self-diagnosis focuses on determining the state of the units which are tested by other system units. In contrast, the suggested approach utilizes the results of tests performed by a system unit to determine its own state. Such diagnosis is in many respects close to self-testing, since a unit evaluates its own state, which is inherent in selftesting. However, as distinct from self-testing, in the suggested approach a unit evaluates it on the basis of tests that it does not performs on itself, but on other system units. The paper considers different diagnosis models with various testing assignments and different faulty assumptions including permanent and intermittent faults, and hybrid-fault situations. The diagnosis algorithm for identifying the unit's state has been developed, and correctness of the algorithm has been verified by computer simulation experiments.

show abstract

Towards Decentralized Fault Detection in UAV Formations

Cited by 17 publications

References 8 publications

Multivariable adaptive distributed leader-follower flight control for multiple UAVs formation

Multivariable adaptive distributed leader-follower flight control for multiple UAVs formation

Distributed Fault Detection and Isolation of Continuous-Time Non-Linear Systems

Method for Unit Self-Diagnosis at System Level

Contact Info

Product

Resources

About