Research on shipborne transfer alignment under the influence of the uncertain disturbance based on the extended state observer

Chen, Weina; Zeng, Qinghua; Liu, Jianye; Hui-zhe, Wang

doi:10.1016/j.ijleo.2016.10.126

Cited by 7 publications

(6 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, the slave and master SINS are always positioned at distance from each other, and there will be relative motion between them while the vehicle turns or manoeuvres, this will introduce additional errors into the TA. The TA method based on inertial measurement matching have received extensive attention in recent years 18,[27][28][29][30][31][32][33][34][35][36][37][38][39][40] , which can be depicted in Fig. 1.…”

Section: Transfer Alignment Of Sinsmentioning

confidence: 99%

Rapid Transfer Alignment of SINS with Measurement Packet Dropping based on a Novel Suboptimal Estimator

Dai

Tang³

et al. 2019

Def. Sc. Jl.

View full text Add to dashboard Cite

Transfer alignment (TA) is an important step for strapdown inertial navigation system (SINS) starting from a moving base, which utilises the information proposed from the higher accurate and well performed master inertial navigation system. But the information is often delayed or even lost in real application, which will seriously affect the accuracy of TA. This paper models the stochastic measurement packet dropping as an independent identically distributed (IID) Bernoulli random process, and introduces it into the measurement equation of rapid TA, and the influence of measurement packet dropping is analysed. Then, it presents a suboptimal estimator for the estimation of the misalignment in TA considering the random arrival of the measurement packet. Simulation has been done for the performance comparison about the suboptimal estimator, standard Kalman filter and minimum mean squared estimator. The results show that the suboptimal estimator has better performance, which can achieve the best TA accuracy. 7. dai, h.; dai, S.; Cong, y.; Zhao, g. & wu, g. Rapid transfer alignment of laser sins using quaternion based angular measurement.

show abstract

Section: Transfer Alignment Of Sinsmentioning

confidence: 99%

Rapid Transfer Alignment of SINS with Measurement Packet Dropping based on a Novel Suboptimal Estimator

Dai

Tang³

et al. 2019

Def. Sc. Jl.

View full text Add to dashboard Cite

show abstract

“…Therefore, the ship’s inertial angular velocity, the dynamic lever-arm acceleration, and the acceleration measured by SINS can be treated as second-order Markov processes. The second-order Markov model of the ship’s inertial angular velocity, the dynamic lever-arm acceleration, and the acceleration measured by SINS can be expressed as follows:

where all the symbols are defined in the previous section and the parameters

, and

in Equations (51)–(53) are identified from our real measurement data, experimental experience, as well as the works from others [ 15 , 32 ]. The parameters

, and

are listed in Table 2 , Table 3 and Table 4 , respectively.…”

Section: Experiments Validationmentioning

confidence: 99%

“…For the dynamic lever-arm, in general, it is considered as white noise during the KF-based TA [ 9 , 11 , 12 ]. At present, most of the existing studies treated the dynamic lever-arm and the ship linear motion as two uncorrelated processes [ 1 , 13 , 14 , 15 ]. However, in our previous shipboard measurements and laboratory experiments, we found a significant bias error in the TA procedure when all the sensors of the INS are of high-quality and the dynamic lever-arm is considered as a second Markov process or a white noise.…”

Section: Introductionmentioning

confidence: 99%

A New Bias Error Prediction Model for High-Precision Transfer Alignment

Zhang

Yang

Qin

et al. 2018

Sensors

View full text Add to dashboard Cite

The purpose of this work was to study bias error in acceleration-based transfer alignment, which is probably caused by cross-correlation between the dynamic lever-arm and the linear motion of a ship. A new prediction model for the cross-correlation-caused error is proposed in this paper. We adopt the Bernoulli-Euler Beam as a simplified ship hull-girder model to verify the existence of the cross-correlation. Then, the mathematical mechanism and the prediction model of the bias error are deduced via the ordinary least squares theory. Three factors influence the bias error in the prediction model: the amplitude of the dynamic lever arm acceleration, the amplitude of the ship motion acceleration, and the cross-correlation between them. Simulation experiments are then conducted to test the influence of the factors. The results show that the mechanism analysis is reasonable, and the bias error prediction model is in agreement with the experimental results. Thus, the proposed prediction model has the potential to deduce the bias error in high-accuracy transfer alignment.

show abstract

“…The bias observers are used for a class of uncertain nonlinear systems [ 39 ]. The ESO is utilized in the active disturbance rejection control [ 40 ], model free predictive current control [ 41 ], incremental actor faults [ 42 ], robust predictive current control [ 43 ], heat pump system [ 44 ], non-iteration predicted entry guidance [ 45 ], active disturbance rejection control [ 46 ], path following control for underactuated vehicles [ 47 ], and shipborne transfer alignment under the uncertain disturbance [ 48 ].…”

Section: Introductionmentioning

confidence: 99%

A Fast North-Finding Algorithm on the Moving Pedestal Based on the Technology of Extended State Observer (ESO)

Bai

Zhang

et al. 2022

Sensors

View full text Add to dashboard Cite

We propose a kind of fast and high-precision alignment algorithm based on the ESO technology. Firstly, in order to solve the problems of rapid, high-accuracy, and anti-interference alignment on the moving pedestal in the north-seeker, the ESO technology in control theory is introduced to improve the traditional Kalman fine-alignment model. This method includes two stages: the coarse alignment in the inertial frame and fine alignment based on the ESO technology. By utilizing the ESO technology, the convergence speed of the heading angle can be greatly accelerated. The advantages of this method are high-accuracy, fast-convergence, strong ability of anti-interference, and short time-cost (no need of KF recursive calculation). Then, the algorithm model, calculation process, and the setting initial-values of the filter are shown. Finally, taking the shipborne north-finder based on the FOG (fiber-optic gyroscope) as the investigated subject, the test on the moving ship is carried out. The results of first off-line simulation show that the misalignment angle of the heading angle of the proposed (traditional) method is ≤2.1′ (1.8′) after 5.5 (10) minutes of alignment. The results of second off-line simulation indicate that the misalignment angle of the heading angle of the proposed (traditional) method is ≤4.8′ (14.2′) after 5.5 (10) minutes of alignment. The simulations are based on the ship-running experimental data. The measurement precisions of Doppler velocity log (DVL) are different in these two experiments.

show abstract

Research on shipborne transfer alignment under the influence of the uncertain disturbance based on the extended state observer

Cited by 7 publications

References 7 publications

Rapid Transfer Alignment of SINS with Measurement Packet Dropping based on a Novel Suboptimal Estimator

Rapid Transfer Alignment of SINS with Measurement Packet Dropping based on a Novel Suboptimal Estimator

A New Bias Error Prediction Model for High-Precision Transfer Alignment

A Fast North-Finding Algorithm on the Moving Pedestal Based on the Technology of Extended State Observer (ESO)

Contact Info

Product

Resources

About