MEMS Gyro Bias Estimation in Accelerated Motions Using Sensor Fusion of Camera and Angular-Rate Gyroscope

Nazemipour, Ali; Manzuri, Mohammad Taghi; Kamran, Danial; Karimian, Mahdi

doi:10.1109/tvt.2020.2976975

Cited by 11 publications

(9 citation statements)

References 35 publications

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“…The expression of the scale factor error coupled to the gyroscope output under the compound rotation modulation scheme, in the b-frame, is shown in Equation (9).…”

Section: Scale Factor Errormentioning

confidence: 99%

“…At the same time, considering the high impact and high rotary velocity of the shell, MEMS sensors have been widely used. However, with the rapid development of high-precision navigation fields, MEMS sensors have larger error defects compared with optical sensors, laser sensors, and other high-precision sensors [9]. According to research, guided projectiles can be discharged at speeds of up to 30 r/s, and the maximum acceleration of guided projectile discharge can reach over 104 m/s 2 [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimal Rotational Angular Velocity Determination Method Based on Compound Rotary Semi-Strapdown Inertial Navigation System

Zhang

Yuan

et al. 2022

Sensors

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Single-axis rotation modulation (SRM) still accumulates errors in the roll axis direction, which leads to the navigation accuracy not meeting the requirements of guided missiles. Compound rotation modulation (CRM) superimposes one-dimensional rotation on the basis of SRM, so that the error of the projectile in the direction of the roll axis is also modulated. However, the error suppression effect of CRM is not only affected by the error of the IMU itself, but also related to the modulation angular velocity. In order to improve the accuracy of rotary semi-strapdown inertial navigation system (RSSINS), this paper proposes an optimal rotation angular velocity determination method. Firstly, the residual error in CRM scheme is analyzed; then, the relationship between the incomplete modulation error and the modulation angular velocity in CRM is discussed; finally, a method for determining the optimal modulation angular velocity is proposed (K-value method). The analysis of the results shows that the navigation accuracy of the guided projectile is effectively improved with the rotation scheme set at the modulation angular velocity determined by the K-value method.

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“…The expression of the scale factor error coupled to the gyroscope output under the compound rotation modulation scheme, in the b-frame, is shown in Equation (9).…”

Section: Scale Factor Errormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal Rotational Angular Velocity Determination Method Based on Compound Rotary Semi-Strapdown Inertial Navigation System

Zhang

Yuan

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…When the external auxiliary information fails, these random errors will lead to a rapid degradation of the accuracy of the MEMS-based integrated navigation system. Therefore, online correction of MEMS gyro biases becomes crucial to improve the reliability and practicality of the MEMS-based integrated navigation system in low-cost applications [5], [6].…”

Section: Introductionmentioning

confidence: 99%

A Global Asymptotically Stable Nonlinear Robust Bias Observer for MEMS Gyros

Chang¹,

Wang²,

Li³

et al. 2022

Preprint

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<p> In practical applications, commercial-grade Micro-Electro-Mechanical System (MEMS) gyros tend to exhibit dynamics sensitivities which are resistant to modeling by linear drift. To address the problem, a new nonlinear robust bias observer (NRBO) is proposed in this paper. Unlike the existing attitude and gyro bias nonlinear observers, we propose a dynamics-sensitive gyro bias decomposition method to tackle unknown nonlinear dynamic models of MEMS gyro bias. We expose the potential advantages of the proposed method: the global asymptotic stability of the NBRO and the robustness to the bias instability of MEMS gyros can be guaranteed through a rational design of the attitude-angular rate nonlinear dynamic coupling (AARNDC) term and the attitude-linear coupling (ALC) term. In addition, the attitude estimation with bias correction is given in the framework of NBRO. The experiment of a cable-parallel robot demonstrate the robustness to the bias instability and the accuracy improvement of MEMS gyros. <br> </p>

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“…In [4], the temperature drift error is compensated to improve accuracy and environmental adaptability in diverse complicated conditions. In [5], a visual gyroscope for sensor fusion system is developed to estimate the operating bias of a micro gyroscope.…”

Section: Introductionmentioning

confidence: 99%

Double Recurrent Perturbation Fuzzy Neural Network Fractional-Order Sliding Mode Control of Micro Gyroscope

2021

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In this paper, a fractional-order sliding mode control method based on a double recurrent perturbation fuzzy neural network (DRPFNN) is proposed for a micro gyroscope system with parameter uncertainty and external disturbance. The DRPFNN is used to realize the adaptive estimation of the unknown part, which ensures that the controller is independent of the accurate mathematical model. The sine-cosine perturbation membership function is used to deal with the uncertainty of rules in neural network, increasing the accuracy, reduce the calculation load, and simplifying computational complexity. In addition, double recurrent links are added to transmit more information with superior dynamic ability. Then, the proposed control system is composed of a DRPFNN estimator and a fractional-order sliding mode controller (FSMC). The fractional calculus operator is introduced into the sliding surface to improve the controller flexibility. The parameter adaptive laws in the neural network are designed to be adaptively stabilized to the optimal value. Finally, simulation studies verify the effectiveness of the proposed control method, showing it can obtain higher control accuracy and enhance robustness than the traditional sliding mode control method. INDEX TERMSDouble recurrent perturbation fuzzy neural network; Recurrent neural network; Sliding mode control; Fractional order; Micro gyroscope.

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MEMS Gyro Bias Estimation in Accelerated Motions Using Sensor Fusion of Camera and Angular-Rate Gyroscope

Cited by 11 publications

References 35 publications

Optimal Rotational Angular Velocity Determination Method Based on Compound Rotary Semi-Strapdown Inertial Navigation System

Optimal Rotational Angular Velocity Determination Method Based on Compound Rotary Semi-Strapdown Inertial Navigation System

A Global Asymptotically Stable Nonlinear Robust Bias Observer for MEMS Gyros

Double Recurrent Perturbation Fuzzy Neural Network Fractional-Order Sliding Mode Control of Micro Gyroscope

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