Joint feedback control and fault diagnosis disambiguation



Published Oct 26, 2023
Ion Matei Maksym Zhenirovsky Johan de Kleer Kai Goebel


This paper proposes a model-based diagnosis approach to detect and isolate intermittent faults in complex systems that operate under feedback control. The feedback control attempts to compensate for model uncertainties and deviations from nominal behavior, but these uncertainties are crucial for accurate fault diagnosis. We focus on faults that are observable only in a particular region of the state space, which is rarely reached in nominal behavior. To address this, we present an approach that considers both control requirements and diagnosis uncertainty in an optimization problem similar to model-predictive control. We compute perturbations on control signals that forces the system to reach states where faults are detectable. We apply our approach to a quadrotor system under motion feedback control, demonstrating the effectiveness of our method. Our approach has the potential to improve the resilience of complex systems by quickly detecting and recovering from disruptive events.

How to Cite

Matei, I., Zhenirovsky, M., de Kleer, J., & Goebel, K. (2023). Joint feedback control and fault diagnosis disambiguation. Annual Conference of the PHM Society, 15(1).
Abstract 65 | PDF Downloads 62



control, diagnosis, modeling, disambiguation

Andersson, C., Akesson, J., & Fuhrer, C. (2016). PyFMI: A python package for simulation of coupled dynamic
models with the functional mock-up interface (Tech. Rep. No. 2). Lund University.

Andersson, J. A. E., Gillis, J., Horn, G., Rawlings, J. B., & Diehl, M. (2019). CasADi – A software framework for nonlinear optimization and optimal control. Mathematical Programming Computation, 11(1), 1–36.

Arulampalam, M. S., Maskell, S., & Gordon, N. (2002). A tutorial on particle filters for online nonlinear/nongaussian bayesian tracking. IEEE TRANSACTIONS ON SIGNAL PROCESSING, 50, 174–188.

Avram, R. (2016). Fault Diagnosis and Fault-Tolerant Control of Quadrotor UAVs (Unpublished doctoral dissertation). Wright State University.

Blochwitz, T., Otter, M., Arnold, M., Bausch, C., Elmqvist, H., Junghanns, A., Clauß, C. (2011). The functional mockup interface for tool independent exchange of simulation models. In Proceedings of the 8th international modelica conference (p. 105-114).

Bouabdallah, S., & Siegwart, R. (2007, Oct). Full control of a quadrotor. In 2007 ieee/rsj international conference on intelligent robots and systems (p. 153-158). doi:10.1109/IROS.2007.4399042

de Kleer, J., Mackworth, A., & Reiter, R. (1992). Characterizing diagnoses and systems. ”Journal of Artificial Inteligence”, 56(2–3), 197–222.

Garcia, C. E., Prett, D. M., & Morari, M. (1989). Model predictive control: Theory and practice - A survey. Automatica, 25(3), 335 - 348.

Gertler, J. (1998). Fault-detection and diagnosis in engineering systems. New York: Marcel Dekker. Gordon, N., Salmond, D., & Smith, A. (1993). Novel approach to nonlinear/non-gaussian bayesian state estimation. IEE Proc. F Radar Signal Process. UK, 140(2), 107.

Guzman-Rabasa, J. A., L ´ opez-Estrada, F. R., Gonzalez ´ Contreras, B. M., Valencia-Palomo, G., Chadli, M., &
Perez-Patricio, M. (2019). Actuator fault detection and isolation on a quadrotor unmanned aerial vehicle modeled as a linear parameter-varying system. Measurement and Control, 52(9-10), 1228-1239.

Isermann, R. (2005). Model-based fault-detection and diagnosis - status and applications. Annual Reviews in Control, 29(1), 71 - 85.

Julier, S. J., & Uhlmann, J. K. (1997, July). New extension of the Kalman filter to nonlinear systems. In I. Kadar (Ed.), Signal processing, sensor fusion, and target recognition vi (Vol. 3068, p. 182-193).

Kalman, R. (1960). A new approach to linear filtering and prediction problems. Transactions of the ASME– Journal of Basic Engineering, 82(Series D), 35–45.

Matei, I., Zhenirovskyy, M., de Kleer, J., & Goebel, K. (2022). A control approach to fault disambiguation.
Annual Conference of the PHM Society, 14(1).

McElhoe, B. A. (1966, July). An Assessment of the Navigation and Course Corrections for a Manned Flyby of Mars or Venus. IEEE Transactions on Aerospace Electronic Systems, 2(4), 613-623.

Patton, R. J., Frank, P. M., & Clark, R. N. (2000). Issues of fault diagnosis for dynamic systems. Springer-Verlag London.

Staroswiecki, M. (2000). Quantitative and qualitative models for fault detection and isolation. Mechanical Systems and Signal Processing, 14(3), 301 - 325.

Staroswiecki, M., & Comtet-Varga, G. (2001). Analytical redundancy relations for fault detection and isolation in algebraic dynamic systems. Automatica, 37(5), 687- 699.
Industry Experience Papers

Most read articles by the same author(s)

1 2 3 4 5 > >>