Novel Transfer Alignment of Shipborne Gimbaled Inertial Navigation Systems

Ding, Zhongli; Cai, Hong; Yang, Huabo

doi:10.2322/tjsass.57.93

Cited by 5 publications

(3 citation statements)

References 18 publications

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“…where K a0 is the accelerometer bias vector; K Ã a0x , K a1x , and K Ã a1x are diagonal matrices, whose components are the accelerometer bias asymmetries, scale factor errors, and scale factor asymmetries, respectively; t is the measurement noise vector. Equations (6)- (10) and (13) have shown that the dynamic model and the measurement model are both high nonlinearity. Besides, these equations indicate that the definition of the platform frame has a significant effect on the system model and the system observability.…”

Section: Isp's Self-ca System Modelsmentioning

confidence: 99%

Observability of self-calibration and self-alignment for inertially stabilized platform

Ding

Zhang

Cai

et al. 2016

Proceedings of the Institution of Mechanical Engineers, Part G:

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The observability of the self-calibration and self-alignment system for an inertially stabilized platform is of vital importance, because it determines the solution existence of the system states. This article provides a straightforward and comprehensible method to investigate the observability of the nonlinear inertially stabilized platform’s self-calibration and self-alignment system. The proposed method is based on a principle that a parameter is observable only if it has a unique solution from the system outputs. The effect of the platform coordinates frame on the system observability is discussed in detail. The demonstration results indicate that the system is completely observable if the platform frame is defined based on the input axes of accelerometer triad. Besides, the analysis processes show that a high performance self-calibration and self-alignment can be accomplished if the inertially stabilized platform is kept stationary with the Earth at different positions and alternately rotated around its each axis. The validation of those results is checked by simulations, and the achieved conclusions make outstanding contributions to the development of the optimal torqueing schemes for the inertially stabilized platform’s self-calibration and self-alignment system.

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Section: Isp's Self-ca System Modelsmentioning

confidence: 99%

Observability of self-calibration and self-alignment for inertially stabilized platform

Ding

Zhang

Cai

et al. 2016

Proceedings of the Institution of Mechanical Engineers, Part G:

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“…The floated inertial platform [1][2][3] is an advanced gimbal-free inertial platform using the hydrostatic supporting system instead of the gimbal system of the traditional inertial platform. 4,5 Generally, it is used as the navigation system of the long-range unmanned vehicle, such as the MX missile. 6 The structure and operating principle of the floated inertial platform have been detailed in Bai et al 7 The floated inertial platform is mainly composed of the sphere, shell and fluid filling the space between the sphere and shell, as shown in Figure 1(a).…”

Section: Introductionmentioning

confidence: 99%

Adaptive neural network control for stabilizing sphere of floated inertial platform using rotation vector

Bai

Zhang

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part G:

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This paper presents a neural network adaptive control scheme for the sphere three-dimensional stabilization of the floated inertial platform in consideration of the shell rotation and unknown attributions. Firstly, based on the analysis of the moments acting on the sphere, the dynamic model of the rotational sphere is established in view of the unknown attributions including mass distribution, hydrodynamic drag, electric brush friction, disturbances etc. Secondly, a radial basis function neural network is designed to identify the real dynamic model. Thirdly, the rotation vector from current attitude to the reference attitude is selected as the input of the controller according to the Euler rotation theorem. An updating law of the control parameters is derived from the radial basis function neural network. The adaptive controller is designed using this rotation vector and the updating law. This method addresses the problem of different dimensions between the two state variables of the dynamic model, which is derived from the sphere attitude expressed by a quaternion. Finally, simulation examples are provided to verify the effectiveness and robustness of the designed controller.

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“…Various methods for in-flight coarse alignment have been researched recently. The conventional methods use an appropriate Kalman filter to estimate the initial attitude based on a linearized error model [5][6][7] or non-linear model [8][9][10] with large heading uncertainty. Contrary to the rapid response requirements, these methods require much time to converge to a stable value.…”

Section: Introductionmentioning

confidence: 99%

Novel In-flight Coarse Alignment of Low-cost Strapdown Inertial Navigation System for Unmanned Aerial Vehicle Applications

Wang

Chen²,

2016

Trans. Japan Soc. Aero. S Sci.

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Kalman filtering-based in-flight alignment heavily relies on coarse alignment to obtain a sound initial attitude; otherwise the subsequent fine alignment cannot achieve a reliable and satisfying result. In order to strengthen the rapid response capability, it is necessary to have an integrated system combining an airborne global navigation satellite system (GNSS) with strapdown inertial navigation system (SINS) to implement coarse alignment on a moving base. Due to complicated flight dynamics and strict load constraints of UAVs, in-flight coarse alignment is much more difficult than ground coarse alignment. Moreover, the introduction of a low-cost micro-electro-mechanical system-based SINS (MEMS-SINS) with high noise, which is suitable for UAV applications with advantages in cost-effectiveness, lightweight, miniature design, low power consumption and survivability, makes it more challenging. In this paper, a novel in-flight coarse alignment aided by GNSS is derived to obtain the initial attitude based on quaternion. Velocity and its differential information from GNSS and specific forces from MEMS-SINS are compared to obtain an analytical solution for the initial angles. A flight test is conducted to test the new algorithm. The results indicate it can achieve 11.5 deg (1·) accuracy for heading, and 5.7 deg (1·) accuracy for level angles (i.e., roll and pitch). As a nice in-flight coarse alignment, it can guarantee accurate and reliable fine alignment afterward.

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Novel Transfer Alignment of Shipborne Gimbaled Inertial Navigation Systems

Cited by 5 publications

References 18 publications

Observability of self-calibration and self-alignment for inertially stabilized platform

Observability of self-calibration and self-alignment for inertially stabilized platform

Adaptive neural network control for stabilizing sphere of floated inertial platform using rotation vector

Novel In-flight Coarse Alignment of Low-cost Strapdown Inertial Navigation System for Unmanned Aerial Vehicle Applications

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