Fault-tolerant pose and inertial parameters estimation of an uncooperative spacecraft based on dual vector quaternions

2018 ◽  
Vol 233 (4) ◽  
pp. 1250-1269 ◽  
Author(s):  
Jianping Yuan ◽  
Xianghao Hou ◽  
Chong Sun ◽  
Yu Cheng
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Formation Flying ◽  
Fault Tolerant ◽  
Estimation Algorithm ◽  
Parameters Estimation ◽  
Small Satellites ◽  
Dual Vector ◽  
Inertial Parameters ◽  
Space Target

Estimating the parameters of an unknown free-floating tumbling spacecraft is an essential task for the on-orbit servicing missions. This paper proposes a dual vector quaternion based fault-tolerant pose and inertial parameters estimation algorithm of an uncooperative space target using two formation flying small satellites. Firstly, by utilizing the dual vector quaternions to model the kinematics and dynamics of the system, not only the representation of the model is concise and compacted, but also the translational and rotational coupled effects are considered. By using this modeling technique along with the measurements from the on-board vision-based sensors, a dual vector quaternion based extended Kalman filter for each of the two small satellites is designed. Secondly, both of the estimations from each small satellite will be used as inputs of the fault-tolerant algorithm. This algorithm is based on the fault-tolerant federal extended Kalman filter strategy to overcome the estimation errors caused by the faulty measurements, the unknown space environment and the computing errors by setting the appropriate ratios of the two estimations from the first step dual vector quaternions extended Kalman filter. Together with the first and second steps, a novel fault-tolerant dual vector quaternions federal extended Kalman filter using two formation flying small satellites is proposed by this paper to estimate the pose and inertial parameters of a free-floating tumbling space target. By utilizing the estimation algorithm, a good prior knowledge of the unknown space target can be achieved. Finally, the proposed dual vector quaternion federal extended Kalman filter is validated by mathematical simulations to show its robust performances.

Novel dual vector quaternions based adaptive extended two-step filter for pose and inertial parameters estimation of a free-floating tumbling space target

2018 ◽  
Vol 233 (7) ◽  
pp. 2570-2591 ◽  
Author(s):  
Xianghao Hou ◽  
Jianping Yuan
Keyword(s):  
Newton Iteration ◽  
Parameters Estimation ◽  
System Model ◽  
Second Step ◽  
Dual Vector ◽  
Inertial Parameters ◽  
Mathematical Simulations ◽  
High Measurement ◽  
Iteration Technique ◽  
Space Target

Estimating the parameters of an uncooperative space target is essential to the on-orbit service missions. A good parameter estimation can provide sufficient prior knowledge for the further operations. This paper proposes a novel dual vector quaternions based adaptive extended two-step filter (DVQ-AETSF) to estimate the pose and inertial parameters of a free-floating tumbling space target. Firstly, both of the rotational and translational motions are modeled by the dual vector quaternions (DVQ). Then, by using the DVQ-based system model, the DVQ-AETSF is designed. The proposed DVQ-AETSF mainly consists of a traditional Kalman filter prediction procedure in the first step and an adaptive regularized Newton iteration technique in the second step. The new proposed two-step filter aims to deal with the high nonlinearities in the measurements equations. By using the proposed DVQ-AETSF, both of the pose and initial parameters of a free floating tumbling space target under large errors of initial guesses and high measurement noise can be well estimated. Finally, the proposed DVQ-AETSF is validated by mathematical simulations to show its performance.

scholarly journals Multichannel Robust Parameters Estimation for Disabled Satellites

2018 ◽  
Vol 36 (5) ◽  
pp. 911-918
Author(s):  
Xianghao Hou ◽  
Jianping Yuan
Keyword(s):  
Kalman Filter ◽  
Complex Space ◽  
Parameters Estimation ◽  
Space Environment ◽  
Kinematics And Dynamics ◽  
Dual Vector ◽  
Monocular Camera ◽  
Inertial Parameters ◽  
The Disabled

Based on the dual vector quaternions, this paper modeled both the kinematics and dynamics of the disabled satellites. In addition, considering the complex space environment may lead to the measurements failure of the target, a novel robust federal Kalman filter based algorithm (DVQ-REKF) is proposed by this paper. By utilizing the designed algorithm, both the pose and inertial parameters can be estimated in the same time when the measurements by each of the monocular camera on the service satellite failed for a certain period of time. Finally, the simulation shows the validity of the designed DVQ-REKF.

scholarly journals A DCM Based Attitude Estimation Algorithm for Low-Cost MEMS IMUs

2015 ◽  
Vol 2015 ◽  
pp. 1-18 ◽  
Author(s):  
Heikki Hyyti ◽  
Arto Visala
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Low Cost ◽  
Direction Cosine ◽  
Estimation Algorithm ◽  
Calibration Method ◽  
Attitude Estimation ◽  
Mems Sensors ◽  
Acceleration Sensors ◽  
Adaptive Extended Kalman Filter

An attitude estimation algorithm is developed using an adaptive extended Kalman filter for low-cost microelectromechanical-system (MEMS) triaxial accelerometers and gyroscopes, that is, inertial measurement units (IMUs). Although these MEMS sensors are relatively cheap, they give more inaccurate measurements than conventional high-quality gyroscopes and accelerometers. To be able to use these low-cost MEMS sensors with precision in all situations, a novel attitude estimation algorithm is proposed for fusing triaxial gyroscope and accelerometer measurements. An extended Kalman filter is implemented to estimate attitude in direction cosine matrix (DCM) formation and to calibrate gyroscope biases online. We use a variable measurement covariance for acceleration measurements to ensure robustness against temporary nongravitational accelerations, which usually induce errors when estimating attitude with ordinary algorithms. The proposed algorithm enables accurate gyroscope online calibration by using only a triaxial gyroscope and accelerometer. It outperforms comparable state-of-the-art algorithms in those cases when there are either biases in the gyroscope measurements or large temporary nongravitational accelerations present. A low-cost, temperature-based calibration method is also discussed for initially calibrating gyroscope and acceleration sensors. An open source implementation of the algorithm is also available.

scholarly journals A Fuzzy State-of-Charge Estimation Algorithm Combining Ampere-Hour and an Extended Kalman Filter for Li-Ion Batteries Based on Multi-Model Global Identification

2018 ◽  
Vol 8 (11) ◽  
pp. 2028 ◽  
Author(s):  
Xin Lai ◽  
Dongdong Qiao ◽  
Yuejiu Zheng ◽  
Long Zhou
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Estimation Algorithm ◽  
State Of Charge ◽  
Distribution Area ◽  
Model Parameters ◽  
Model Accuracy ◽  
Soc Estimation ◽  
Fuzzy Fusion ◽  
Fuzzy State

The popular and widely reported lithium-ion battery model is the equivalent circuit model (ECM). The suitable ECM structure and matched model parameters are equally important for the state-of-charge (SOC) estimation algorithm. This paper focuses on high-accuracy models and the estimation algorithm with high robustness and accuracy in practical application. Firstly, five ECMs and five parameter identification approaches are compared under the New European Driving Cycle (NEDC) working condition in the whole SOC area, and the most appropriate model structure and its parameters are determined to improve model accuracy. Based on this, a multi-model and multi-algorithm (MM-MA) method, considering the SOC distribution area, is proposed. The experimental results show that this method can effectively improve the model accuracy. Secondly, a fuzzy fusion SOC estimation algorithm, based on the extended Kalman filter (EKF) and ampere-hour counting (AH) method, is proposed. The fuzzy fusion algorithm takes advantage of the advantages of EKF, and AH avoids the weaknesses. Six case studies show that the SOC estimation result can hold the satisfactory accuracy even when large sensor and model errors exist.

scholarly journals Satellite Inertia Parameters Estimation Based on Extended Kalman Filter

2019 ◽  
Author(s):  
Abdellatif Bellar ◽  
Mohammed Arezki Si Mohammed
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Critical Role ◽  
Low Earth Orbit ◽  
Parameters Estimation ◽  
Moment Of Inertia ◽  
Satellite Mission ◽  
Inertia Matrix ◽  
Inertia Parameters ◽  
The Moment

The moment of inertia parameters play a critical role in assuring the spacecraft mission throughout its lifetime. However, determination of the moment of inertia is a key challenge in operating satellites. During satellite mission, those parameters can change in orbit for many reasons such as sloshing, fuel consumption, etc. Therefore, the inertia matrix should be estimated in orbit to enhance the attitude estimation and control accuracy. This paper investigates the use of gyroscope to estimate the attitude rate and inertia matrix for low earth orbit satellite via extended Kalman filter. Simulation results show the effectiveness and advantages of the proposed algorithm in estimating these parameters without knowing the nominal inertia. The robustness of the proposed algorithm has been validated using the Monte-Carlo method. The obtained results demonstrate that the accuracy of the estimated inertia and angular velocity parameters is satisfactory for satellite with coarse accuracy mission requirements. The proposed method can be used for different types of satellites.

scholarly journals Fault Diagnosis of PMSG Stator Inter-Turn Fault Using Extended Kalman Filter and Unscented Kalman Filter

Energies ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 2972 ◽  
Author(s):  
Waseem El Sayed ◽  
Mostafa Abd El Geliel ◽  
Ahmed Lotfy
Keyword(s):  
Kalman Filter ◽  
Fault Diagnosis ◽  
Extended Kalman Filter ◽  
Continuous Monitoring ◽  
Unscented Kalman Filter ◽  
Fault Tolerant ◽  
Synchronous Generator ◽  
Operating Conditions ◽  
Practical Implementation ◽  
Complete Failure

Since the permeant magnet synchronous generator (PMSG) has many applications in particular safety-critical applications, enhancing PMSG availability has become essential. An effective tool for enhancing PMSG availability and reliability is continuous monitoring and diagnosis of the machine. Therefore, designing a robust fault diagnosis (FD) and fault tolerant system (FTS) of PMSG is essential for such applications. This paper describes an FD method that monitors online stator winding partial inter-turn faults in PMSGs. The fault appears in the direct and quadrature (dq)-frame equations of the machine. The extended Kalman filter (EKF) and unscented Kalman filter (UKF) were used to detect the percentage and the place of the fault. The proposed techniques have been simulated for different fault scenarios using Matlab®/Simulink®. The results of the EKF estimation responses simulation were validated with the practical implementation results of tests that were performed with a prototype PMSG used in the Arab Academy For Science and Technology (AAST) machine lab. The results showed impressive responses with different operating conditions when exposed to different fault states to prevent the development of complete failure.

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