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Research Papers

Redundant MEMS-Based Inertial Navigation Using Nonlinear Observers

[+] Author and Article Information
Robert. H. Rogne

Department of Engineering Cybernetics,
Centre for Autonomous Marine Operations
and Systems (NTNU AMOS),
Norwegian University of Science
and Technology,
Trondheim N-7491, Norway
e-mail: robert.rogne@ntnu.no

Torleiv. H. Bryne

Department of Engineering Cybernetics,
Centre for Autonomous Marine Operations
and Systems (NTNU AMOS),
Norwegian University of Science
and Technology,
Trondheim N-7491, Norway
e-mail: torleiv.h.bryne@ntnu.no

Thor. I. Fossen

Department of Engineering Cybernetics,
Centre for Autonomous Marine Operations
and Systems (NTNU AMOS),
Norwegian University of Science
and Technology,
Trondheim N-7491, Norway
e-mail: thor.fossen@ntnu.no

Tor. A. Johansen

Department of Engineering Cybernetics,
Centre for Autonomous Marine Operations
and Systems (NTNU AMOS),
Norwegian University of Science
and Technology,
Trondheim N-7491, Norway
e-mail: tor.arne.johansen@ntnu.no

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received January 13, 2017; final manuscript received November 15, 2017; published online January 9, 2018. Assoc. Editor: Soo Jeon.

J. Dyn. Sys., Meas., Control 140(7), 071001 (Jan 09, 2018) (7 pages) Paper No: DS-17-1023; doi: 10.1115/1.4038647 History: Received January 13, 2017; Revised November 15, 2017

We present two alternative methods for fault detection and isolation (FDI) with redundant Microelectromechanical system (MEMS) inertial measurement units (IMUs) in inertial navigation systems (INS) based on nonlinear observers (NLOs). The first alternative is based on the parity space method, while the second is expanded with quaternion-based averaging and FDI. Both alternatives are implemented and validated using data gathered in a full-scale experiment on an offshore vessel. Data from three identical MEMS IMUs and the vessel's own industrial sensors are used to verify the methods' FDI capabilities. The results reveal that when it comes to FDI of the IMUs' angular rate sensors, there are differences between the two methods. The navigation algorithm based on quaternion weighting is essentially unaffected by the failure of an angular rate sensor, while the parity-space-method-based alternative experiences a perturbation.

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Figures

Grahic Jump Location
Fig. 2

Fault detection for accelerometer fault. Parity space method.

Grahic Jump Location
Fig. 3

Attitude estimation error. Parity space method for angular rate sensor (alternative 1).

Grahic Jump Location
Fig. 4

Fault detection for angular rate sensor fault. Parity space method (alternative 1).

Grahic Jump Location
Fig. 7

Gyro bias estimates. Parity space method for angular rate sensor (alternative 1).

Grahic Jump Location
Fig. 1

Redundant IMU alternative 2—observer structure

Grahic Jump Location
Fig. 5

Attitude estimation error. Quaternion FDI method angular rate sensor (alternative 2).

Grahic Jump Location
Fig. 6

Fault detection for angular rate sensor fault. Quaternion FDI (alternative 2).

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