Particle Filter for Fault Diagnosis and Robust Navigation of Underwater Robot
| Autor: | Mogens Blanke, Fredrik Dukan, Bo Zhao, Roger Skjetne |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2014 |
| Předmět: |
Engineering
business.industry Fault Tolerance Fault tolerance Remotely operated underwater vehicle Control and Systems Engineering Robustness (computer science) ROV Particle filter Underwater navigation Underwater robot Switch-mode hidden Markov model Electrical and Electronic Engineering business Hidden Markov model Simulation Fault diagnosis |
| Zdroj: | Zhao, B, Skjetne, R, Blanke, M & Dukan, F 2014, ' Particle Filter for Fault Diagnosis and Robust Navigation of Underwater Robot ', I E E E Transactions on Control Systems Technology, vol. 22, no. 6, pp. 2399 – 2407 . https://doi.org/10.1109/TCST.2014.2300815 |
| Popis: | This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. © IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. A particle filter (PF)-based robust navigation with fault diagnosis (FD) is designed for an underwater robot, where 10 failure modes of sensors and thrusters are considered. The nominal underwater robot and its anomaly are described by a switching-mode hidden Markov model. By extensively running a PF on the model, the FD and robust navigation are achieved. Closed-loop full-scale experimental results show that the proposed method is robust, can diagnose faults effectively, and can provide good state estimation even in cases where multiple faults occur. Comparing with other methods, the proposed method can diagnose all faults within a single structure, it can diagnose simultaneous faults, and it is easily implemented. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|