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A novel artificial fish swarm algorithm for recalibration of fiber optic gyroscope error parameters.

Gao Y, Guan L, Wang T, Sun Y - Sensors (Basel) (2015)

Bottom Line: To solve these weak points, functional behaviors and the overall procedures of AFSA have been improved with some parameters eliminated and several supplementary parameters added.After that, the NAFSA is verified with simulation and experiments and its priorities are compared with that of the conventional calibration method and optimal AFSA.Results demonstrate high efficiency of the NAFSA on FOG error coefficients recalibration.

View Article: PubMed Central - PubMed

Affiliation: Institute of Inertial Navigation and Measurement & Control Technology, College of Automation, Harbin Engineering University, Harbin 150001, China. gaoyanbin@hrbeu.edu.cn.

ABSTRACT
The artificial fish swarm algorithm (AFSA) is one of the state-of-the-art swarm intelligent techniques, which is widely utilized for optimization purposes. Fiber optic gyroscope (FOG) error parameters such as scale factors, biases and misalignment errors are relatively unstable, especially with the environmental disturbances and the aging of fiber coils. These uncalibrated error parameters are the main reasons that the precision of FOG-based strapdown inertial navigation system (SINS) degraded. This research is mainly on the application of a novel artificial fish swarm algorithm (NAFSA) on FOG error coefficients recalibration/identification. First, the NAFSA avoided the demerits (e.g., lack of using artificial fishes' pervious experiences, lack of existing balance between exploration and exploitation, and high computational cost) of the standard AFSA during the optimization process. To solve these weak points, functional behaviors and the overall procedures of AFSA have been improved with some parameters eliminated and several supplementary parameters added. Second, a hybrid FOG error coefficients recalibration algorithm has been proposed based on NAFSA and Monte Carlo simulation (MCS) approaches. This combination leads to maximum utilization of the involved approaches for FOG error coefficients recalibration. After that, the NAFSA is verified with simulation and experiments and its priorities are compared with that of the conventional calibration method and optimal AFSA. Results demonstrate high efficiency of the NAFSA on FOG error coefficients recalibration.

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Related in: MedlinePlus

Indicator functions of SAFSA, OAFSA and NAFSA.
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sensors-15-10547-f004: Indicator functions of SAFSA, OAFSA and NAFSA.

Mentions: Furthermore, the variation tendencies of indicator functions among the SAFSA, OAFSA and NAFSA, when they are used for identifying the FOG scale factors, are demonstrated in Figure 4.


A novel artificial fish swarm algorithm for recalibration of fiber optic gyroscope error parameters.

Gao Y, Guan L, Wang T, Sun Y - Sensors (Basel) (2015)

Indicator functions of SAFSA, OAFSA and NAFSA.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4481985&req=5

sensors-15-10547-f004: Indicator functions of SAFSA, OAFSA and NAFSA.
Mentions: Furthermore, the variation tendencies of indicator functions among the SAFSA, OAFSA and NAFSA, when they are used for identifying the FOG scale factors, are demonstrated in Figure 4.

Bottom Line: To solve these weak points, functional behaviors and the overall procedures of AFSA have been improved with some parameters eliminated and several supplementary parameters added.After that, the NAFSA is verified with simulation and experiments and its priorities are compared with that of the conventional calibration method and optimal AFSA.Results demonstrate high efficiency of the NAFSA on FOG error coefficients recalibration.

View Article: PubMed Central - PubMed

Affiliation: Institute of Inertial Navigation and Measurement & Control Technology, College of Automation, Harbin Engineering University, Harbin 150001, China. gaoyanbin@hrbeu.edu.cn.

ABSTRACT
The artificial fish swarm algorithm (AFSA) is one of the state-of-the-art swarm intelligent techniques, which is widely utilized for optimization purposes. Fiber optic gyroscope (FOG) error parameters such as scale factors, biases and misalignment errors are relatively unstable, especially with the environmental disturbances and the aging of fiber coils. These uncalibrated error parameters are the main reasons that the precision of FOG-based strapdown inertial navigation system (SINS) degraded. This research is mainly on the application of a novel artificial fish swarm algorithm (NAFSA) on FOG error coefficients recalibration/identification. First, the NAFSA avoided the demerits (e.g., lack of using artificial fishes' pervious experiences, lack of existing balance between exploration and exploitation, and high computational cost) of the standard AFSA during the optimization process. To solve these weak points, functional behaviors and the overall procedures of AFSA have been improved with some parameters eliminated and several supplementary parameters added. Second, a hybrid FOG error coefficients recalibration algorithm has been proposed based on NAFSA and Monte Carlo simulation (MCS) approaches. This combination leads to maximum utilization of the involved approaches for FOG error coefficients recalibration. After that, the NAFSA is verified with simulation and experiments and its priorities are compared with that of the conventional calibration method and optimal AFSA. Results demonstrate high efficiency of the NAFSA on FOG error coefficients recalibration.

Show MeSH
Related in: MedlinePlus