Abstract
In this paper, we propose a fault type classification algorithm for a networked multi-robot formation control. Both actuator and sensor faults of a robot are considered as node fault on the networked system. The Support Vector Machine (SVM) based classification scheme is proposed in order to classify the fault type accurately. Basically, the graph-theoretic approach is used for modeling the multi-agent communication and to generate the formation control law. A numerical simulation is presented to confirm the performance of proposed fault type classification method.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Fax, J.A.: Information flow and cooperative control of vehicle formations. IEEE Trans. Autom. Control 49(9), 1465–1476 (2004)
Olfati-Saber, R., Fax, J.A., Murray, R.M.: Consensus and cooperation in networked multi-agent systems. Proc. IEEE 95(1), 215–233 (2007)
Aldeen, M., Crusca, F.: Observer-based fault detection and identification scheme for power systems. IEEE Proc Gener. Transm. Distrib. 153(1), 71–79 (2006)
Hwang, I., et al.: A survey of fault detection, isolation, and reconfiguration methods. IEEE Trans. Control Syst. Technol. 18(3), 636–653 (2010)
Kwon, C., Liu, W., Hwang, I.: Security analysis for cyber-physical systems against stealthy deception attacks. American Control Conference (ACC), 2013. IEEE (2013)
Negash, L., Kim, S.-H., Choi, H.-L.: Distributed unknown-input-observers for cyber attack detection and isolation in formation flying UAVs (2017), arXiv:1701.06325
Alsafasfeh, Q.H., Abdel-Qader, I., Harb, A.M.: Fault classification and localization in power systems using fault signatures and principal components analysis. Energy Power Eng. 4(06), 506 (2012)
Dash, P.K., Samantaray, S.R., Panda, G.: Fault classification and section identification of an advanced series-compensated transmission line using support vector machine. IEEE Trans. Power Deliv. 22(1), 67–73 (2007)
Silva, K.M., Souza, B.A.: Fault detection and classification in transmission lines based on wavelet transform and ANN. IEEE Trans. Power Deliv. 21(4), 2058–2063 (2006)
Kim, Sang-Hyeon, Negash, Lebsework, Choi, Han-Lim: Cubature Kalman filter based fault detection and isolation for formation control of multi-UAVs. IFAC-PapersOnLine 49(15), 63–68 (2016)
Shames, I., et al.: Distributed fault detection for interconnected second-order systems. Automatica 47(12), 2757–2764 (2011)
Isermann, R.: Model-based fault-detection and diagnosisstatus and applications. Annu. Rev. control 29(1), 71–85 (2005)
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)
Beck, J.V., Arnold, K.J.: Parameter Estimation in Engineering and Science. James Beck (1977)
Acknowledgements
This work was supported by the ICT R&D program of MSIP/IITP. [R-20150223-000167, Development of High Reliable Communications and Security SW for Various Unmanned Vehicles].
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Kim, SH., Negash, L., Choi, HL. (2019). SVM-Based Fault Type Classification Method for Navigation of Formation Control Systems. In: Kim, JH., et al. Robot Intelligence Technology and Applications 5. RiTA 2017. Advances in Intelligent Systems and Computing, vol 751. Springer, Cham. https://doi.org/10.1007/978-3-319-78452-6_13
Download citation
DOI: https://doi.org/10.1007/978-3-319-78452-6_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-78451-9
Online ISBN: 978-3-319-78452-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)