Coupling-Characterization-Based Robust Attitude Control Scheme for Hypersonic Vehicles

Guo, Zhishan; Zhou, Jun; Guo, Jianguo; Cieslak, Jérôme; Chang, Jing

doi:10.1109/tie.2017.2682031

Cited by 70 publications

(21 citation statements)

References 31 publications

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“…A judging condition is presented here: Assume

is the normal vector of the tracking plane

is the unit vector parallel to the axes,

is the vectorial angle between

and

where

is a sufficiently small value,

is the Euclidean metric of vector [ 31 ]. If the inequality satisfies, the situation in Remark 2 will happen.…”

Section: Target Tracking With Error Compensationmentioning

confidence: 99%

Improved Spatial Registration and Target Tracking Method for Sensors on Multiple Missiles

Lü

Xie

Zhou

2018

Sensors

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Inspired by the problem that the current spatial registration methods are unsuitable for three-dimensional (3-D) sensor on high-dynamic platform, this paper focuses on the estimation for the registration errors of cooperative missiles and motion states of maneuvering target. There are two types of errors being discussed: sensor measurement biases and attitude biases. Firstly, an improved Kalman Filter on Earth-Centered Earth-Fixed (ECEF-KF) coordinate algorithm is proposed to estimate the deviations mentioned above, from which the outcomes are furtherly compensated to the error terms. Secondly, the Pseudo Linear Kalman Filter (PLKF) and the nonlinear scheme the Unscented Kalman Filter (UKF) with modified inputs are employed for target tracking. The convergence of filtering results are monitored by a position-judgement logic, and a low-pass first order filter is selectively introduced before compensation to inhibit the jitter of estimations. In the simulation, the ECEF-KF enhancement is proven to improve the accuracy and robustness of the space alignment, while the conditional-compensation-based PLKF method is demonstrated to be the optimal performance in target tracking.

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“…A judging condition is presented here: Assume

is the normal vector of the tracking plane

is the unit vector parallel to the axes,

is the vectorial angle between

and

where

is a sufficiently small value,

is the Euclidean metric of vector [ 31 ]. If the inequality satisfies, the situation in Remark 2 will happen.…”

Section: Target Tracking With Error Compensationmentioning

confidence: 99%

Improved Spatial Registration and Target Tracking Method for Sensors on Multiple Missiles

Lü

Xie

Zhou

2018

Sensors

Self Cite

View full text Add to dashboard Cite

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“…The disturbance observer‐based controller (DOBC) design is widely researched for unmatched uncertain system 25‐29 in the past decade. Chen 30 give a thorough review on the DOBC and related methods.…”

Section: Introductionmentioning

confidence: 99%

Sliding mode control for systems subjected to unmatched disturbances/unknown control direction and its application

Guo

Wang

et al. 2020

Intl J Robust & Nonlinear

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This article investigates the control issue for systems subjected to unmatched disturbances and unknown control direction simultaneously. A class of adaptive nonlinear disturbance observer is developed to estimate the unmatched disturbances precisely and then extended to the general high‐order form. The proposed method guarantees the stability of the second‐order and high‐order system under unknown control direction and robustness against the unmatched disturbances with the help of the sliding mode technique. The application in the integrated missile guidance and control (IGC) system design demonstrates the validity of the proposed control method. Herein, the cause of unknown control direction and the influence of the time‐vary control gain on the missile are discussed in detail. Also, the Monte Carlo campaign is run to show the superiority of the developed control strategy compared with the existing IGC method.

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“…With the advancement of performance in flight distance, speed and maneuvering penetration ability, hypersonic vehicle puts forward more stringent requirements for the reliability, adaptability and accuracy of inertial navigation system (INS) [1], [2]. Due to the drift in accelerometer and gyroscope, the navigation error of INS will accumulate with time, which will seriously affect the navigation accuracy of hypersonic vehicle.…”

Section: Introductionmentioning

confidence: 99%

Identification Method of SINS Error Parameters Utilizing Artificial Neural Network for Hypersonic Vehicle

Guo

Zhang

et al. 2019

IEEE Access

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To improve the navigation accuracy of hypersonic vehicle, an error parameter identification method of strap-down inertial navigation system (SINS) based on artificial neural network is proposed. Firstly, the inertial measurement unit (IMU) error model and the SINS navigation calculation model are established, which can provide an accurate model basis for the error parameters identification. Then, four kinds of neural network structures with different inputs are constructed and optimized by the numerical simulation. To further improve the identification accuracy of the error parameters, the neural network method is improved by adjusting the inputs of the network structure and optimizing the initial network values based on gradient particle swarm optimization. The improved neural network method is eventually used to identify the SINS error parameters of hypersonic vehicle. The simulation results show that the neural network with position deviation, velocity deviation and attitude deviation as the inputs has the optimal network structure. The improved artificial neural network method has the highest identification accuracy for the error parameters compared with other methods, and the relative errors of the error parameters are less than 34%. Meanwhile, the navigation errors of the proposed method are equivalent to the magnitude of Kalman filter algorithm, which demonstrates the effectiveness of our method to identify the high-precision SINS error parameters and improve the navigation accuracy for hypersonic vehicle.

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Coupling-Characterization-Based Robust Attitude Control Scheme for Hypersonic Vehicles

Cited by 70 publications

References 31 publications

Improved Spatial Registration and Target Tracking Method for Sensors on Multiple Missiles

Improved Spatial Registration and Target Tracking Method for Sensors on Multiple Missiles

Sliding mode control for systems subjected to unmatched disturbances/unknown control direction and its application

Identification Method of SINS Error Parameters Utilizing Artificial Neural Network for Hypersonic Vehicle

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