Fault Detection and Adaptive Parameter Estimation with Quantum Inspired Techniques and Multiple-Model Filters


Fault detection and quick and adaptive estimation of parameters to be used in controllers are crucial to successful operation of the plants. In fact, when a fault occurs, parameters of a system can change drastically; in order to cope with such changes, a quantum-boost scheme for multiple-model Kalman filter is presented in this study. In fault detection, it is of vital significance that the posterior probability corresponding to the best model rises and converges quickly. This can be achieved with the extended Grover's algorithm, originally developed for quantum information processing. Efficacy of the quantum-boosted multiple-model Kalman filter is tested in this study with two examples. In both examples, the quantum boost scheme is seen to accelerate the rise and convergence of the probability corresponding to best model for the unknown initial parameter and also the changed parameter. With quantum-boosted multiple-model Kalman filter, fast fault detection can be achieved by monitoring the probabilities of the assumed values of the parameters.

Meeting Name

2018 AIAA Guidance, Navigation, and Control Conference, AIAA SciTech Forum, MGNC 2018 (2018: Jan. 8-12, Kissimmee, FL)


Mechanical and Aerospace Engineering


This study was partially supported by the Air Force grant AFOSR FA9550-15-1-0343.

Keywords and Phrases

Aviation; Bandpass filters; Kalman filters; Navigation; Parameter estimation; Probability; Quantum optics; Adaptive estimation; Adaptive parameter estimation; Initial parameter; Multiple model filters; Multiple model Kalman filter; Posterior probability; Quantum-information processing; S-algorithms; Fault detection

International Standard Book Number (ISBN)


Document Type

Article - Conference proceedings

Document Version


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