[
Cayero, J., Rotondo, D., Morcego, B. and Puig, V. (2019). Optimal state observation using quadratic boundedness: Application to UAV disturbance estimation, International Journal of Applied Mathematics and Computer Science 29(1): 99–109, DOI: 10.2478/amcs-2019-0008.10.2478/amcs-2019-0008
]Search in Google Scholar
[
Chabir, K., Rhouma, T., Keller, J.Y. and Sauter, D. (2018). State filtering for networked control systems subject to switching disturbances, International Journal of Applied Mathematics and Computer Science 28(3): 473–482, DOI: 10.2478/amcs-2018-0036.10.2478/amcs-2018-0036
]Search in Google Scholar
[
Chen, G., Wang, J. and Shieh, L.S. (1997). Interval Kalman filtering, IEEE Transactions on Aerospace and Electronic Systems 33(1): 250–259.10.1109/7.570759
]Search in Google Scholar
[
Combastel, C. (2015). Merging Kalman filtering and zonotopic state bounding for robust fault detection under noisy environment, Proceedings of the 9th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes, Paris, France, pp. 289–295.
]Search in Google Scholar
[
Combastel, C. (2016). An Extended Zonotopic and Gaussian Kalman Filter (EZGKF) merging set-membership and stochastic paradigms: Toward non-linear filtering and fault detection, Annual Reviews in Control 42: 232–243.10.1016/j.arcontrol.2016.07.002
]Search in Google Scholar
[
Ingimundarson, A., Manuel Bravo, J., Puig, V., Alamo, T. and Guerra, P. (2009). Robust fault detection using zonotope-based set-membership consistency test, International Journal of Adaptive Control And Signal Processing 23(4): 311–330.10.1002/acs.1038
]Search in Google Scholar
[
Jaulin, L., Braems, I., Kieffer, M. and Walter, E. (2001a). Nonlinear state estimation using forward-backward propagation of intervals in an algorithm, in W. Krämer and J.W. von Gudenberg (Eds), Scientific Computing, Validated Numerics, Interval Methods, Springer US, New York, pp. 191–204..10.1007/978-1-4757-6484-0_16
]Search in Google Scholar
[
Jaulin, L., Kieffer, M., Didrit, O. and Walter, E. (2001b). Applied Interval Analysis with Examples in Parameter and State Estimation, Robust Control and Robotics, Springer, London.10.1007/978-1-4471-0249-6
]Search in Google Scholar
[
Kalman, R.E. (1960). A new approach to linear filtering and prediction problems, Journal of Basic Engineering 82(Series D): 35–45.10.1115/1.3662552
]Search in Google Scholar
[
Kieffer, M., Jaulin, L., Walter, E. and Meizel, D. (1999). Guaranteed mobile robot tracking using interval analysis, Proceedings of the Workshop on Application of Interval Analysis to System and Control, Girona, Spain, pp. 347–360.
]Search in Google Scholar
[
Lesecq, S., Barraud, A. and Dinh, K. (2003). Numerical accurate computations for ellipsoidal state bounding, Proceedings of the Mediterranean Conference on Control and Automation (MED’03), Rhodes, Greece.
]Search in Google Scholar
[
Masreliez, C. andMartin, R. (1977). Robust Bayesian estimation for the linear model and robustifying the Kalman filter, IEEE Transactions on Automatic Control 22(3): 361–371.10.1109/TAC.1977.1101538
]Search in Google Scholar
[
Moore, R.E. (1959). Automatic error analysis in digital computation, Technical report LMSD-48421, Lockheed Missiles and Space Co, Palo Alto.
]Search in Google Scholar
[
Moore, R.E. (1966). Interval Analysis, Prentice-Hall, Englewood Cliffs.
]Search in Google Scholar
[
Moore, R., Kearfott, R. and Cloud, M. (2009). Introduction to Interval Analysis, Society for Industrial and Applied Mathematics, Philadelphia.10.1137/1.9780898717716
]Search in Google Scholar
[
Raka, S.-A. and Combastel, C. (2013). Fault detection based on robust adaptive thresholds: A dynamic interval approach, Annual Reviews in Control 37(1): 119–128.10.1016/j.arcontrol.2013.04.001
]Search in Google Scholar
[
Ribot, P., Jauberthie, C. and Trave-Massuyés, L. (2007). State estimation by interval analysis for a nonlinear differential aerospace model, Proceedings of the European Control Conference, Kos, Greece, pp. 4839–4844.
]Search in Google Scholar
[
Rump, S.M. (1999). INTLAB—INTerval LABoratory, in T. Csendes (Ed.), Developments in Reliable Computing, Kluwer Academic Publishers, Dordrecht, pp. 77–104.10.1007/978-94-017-1247-7_7
]Search in Google Scholar
[
Tran, T.A. (2017). Cadre unifié pour la modélisation des incertitudes tatistiques et bornés: application à la détection et isolation de défauts dans les systémes dynamiques incertains par estimation PhD thesis, Université Toulouse 3—Paul Sabatier, Toulouse, www.theses.fr/2017TO U30292.
]Search in Google Scholar
[
Tran, T.A., Jauberthie, C., Le Gall, F. and Travé-Massuyès, L. (2017). Interval Kalman filter enhanced by positive definite upper bounds, Proceedings of the 20th IFACWorld Congress, Toulouse, France, pp. 1595–1600.
]Search in Google Scholar
[
Tran, T. A., Le Gall, F., Jauberthie, C. and Travé-Massuyès, L. (2016). Two stochastic filters and their interval extensions, Proceedings of the 4th IFAC International Conference on Intelligent Control and Automation Sciences, Reims, France, pp. 49–54.
]Search in Google Scholar
[
Welch, G. and Bishop, G. (2001). An introduction to the Kalman filter, SIGGRAPH, Los Angeles, USA, Course 8.
]Search in Google Scholar
[
Xiong, J., Jauberthie, C., Travé-Massuyés, L. and Le Gall, F. (2013). Fault detection using interval Kalman filtering enhanced by constraint propagation, Proceedings of the IEEE 52nd Annual Conference on Decision and Control (CDC), Florence, Italy, pp. 490–495.
]Search in Google Scholar
