Multi-layer architecture for adaptive fuzzy inference system with a large number of input features

2017 ◽  
Vol 42 ◽  
pp. 23-41 ◽  
Author(s):  
Mohammad Saber Iraji
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Adaptive Fuzzy

scholarly journals A Novel Alignment Method for SINS with Large Misalignment Angles Based on EKF2 and AFIS

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 5975 ◽  
Author(s):  
Yanming Zhao ◽  
Gongmin Yan ◽  
Yongyuan Qin ◽  
Qiangwen Fu
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Linear Measurement ◽  
State Equation ◽  
Error Model ◽  
Second Order ◽  
Alignment Method ◽  
Inference System ◽  
Adaptive Fuzzy ◽  
Attitude Error

In order to achieve the fine alignment of strapdown inertial navigation (SINS) under large misalignment angles, a novel filtering alignment method is proposed based on the second-order extended Kalman filter (EKF2) and adaptive fuzzy inference system (AFIS). Firstly, the quaternion is employed to represent the attitude errors of SINS. A second-order nonlinear state equation is made based on the nonlinear velocity error model and attitude error model, and the linear measurement equation is based on the velocity outputs from SINS. Then, the filtering scheme is designed based on EKF2 and AFIS. The error estimation and fine alignment can be achieved by using the proposed filtering scheme. The results of Monte Carlo Simulation show that the errors of pitch, roll and yaw misalignment angles quickly decrease to about 14″, 15″ and 7.62′ respectively in 350 s under the condition of any misalignment angles with pitch error from −40° to 40°, roll error from −40° to 40°, and yaw error from −50° to 50°. Even when the initial misalignment angles are all very large such as (80°, 120°, 170°), the proposed nonlinear alignment method still can converge normally by utilizing the adaptive fuzzy inference system (AFIS) to adjust the covariance matrix Pk/k−1. Finally, the turntable experiment was performed, and the effectiveness and superiority of the proposed method were further verified by compared with other nonlinear methods.

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