scholarly journals Autonomous Orbit Determination for Lagrangian Navigation Satellite Based on Neural Network Based State Observer

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Youtao Gao ◽  
Tanran Zhao ◽  
Bingyu Jin ◽  
Junkang Chen ◽  
Bo Xu
Keyword(s):  
Neural Network ◽  
Orbit Determination ◽  
Transition Matrix ◽  
State Observer ◽  
Nonlinear Perturbations ◽  
The Neural Network ◽  
Autonomous Orbit Determination ◽  
Simulation Results ◽  
Libration Orbits

In order to improve the accuracy of the dynamical model used in the orbit determination of the Lagrangian navigation satellites, the nonlinear perturbations acting on Lagrangian navigation satellites are estimated by a neural network. A neural network based state observer is applied to autonomously determine the orbits of Lagrangian navigation satellites using only satellite-to-satellite range. This autonomous orbit determination method does not require linearizing the dynamical mode. There is no need to calculate the transition matrix. It is proved that three satellite-to-satellite ranges are needed using this method; therefore, the navigation constellation should include four Lagrangian navigation satellites at least. Four satellites orbiting on the collinear libration orbits are chosen to construct a constellation which is used to demonstrate the utility of this method. Simulation results illustrate that the stable error of autonomous orbit determination is about 10 m. The perturbation can be estimated by the neural network.

scholarly journals Integrated Design of Autonomous Orbit Determination and Orbit Control for GEO Satellite Based on Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Youtao Gao ◽  
Zhicheng You ◽  
Bo Xu
Keyword(s):  
Neural Network ◽  
Orbit Determination ◽  
Design Method ◽  
Integrated Design ◽  
Satellite System ◽  
Orbit Control ◽  
Neural Network Controller ◽  
The Neural Network ◽  
Geo Satellite ◽  
Autonomous Orbit Determination

In order to improve the autonomy of a maneuvered GEO satellite which is a member of a navigation satellite system, an integrated design method of autonomous orbit determination and autonomous control was proposed. A neural network state observer was designed to estimate the state of the GEO satellite, with only the intersatellite ranging information as observations. The controller is determined autonomously by another neural network based on the estimated state and the preset correction trajectory. A gradient descent learning method with a forgetting factor was used to derive the weight updating strategy which can satisfy the system’s stability and real-time performance. A Lyapunov method was used to prove the stability of both the observer and the controller. The neural network observer can reduce the influence of control on autonomous orbit determination. The neural network controller can improve the robustness of the maneuvered GEO satellite. The simulation results show the effectiveness of this method.

A New Algorithm for Onboard Autonomous Orbit Determination of Navigation Satellites

2011 ◽  
Vol 64 (S1) ◽  
pp. S162-S179 ◽  
Author(s):  
Haihong Wang ◽  
Zhonggui Chen ◽  
Jinjun Zheng ◽  
Haibin Chu
Keyword(s):  
Data Processing ◽  
Orbit Determination ◽  
Transition Matrix ◽  
State Transition ◽  
State Transition Matrix ◽  
Processing Unit ◽  
Data Processing Unit ◽  
Autonomous Orbit Determination ◽  
Computing Method

Autonomous orbit determination of a navigation constellation is the process by which the orbit parameters of navigation satellites are autonomously calibrated onboard the satellites without the need for external aids. It commonly uses a satellite onboard data processing unit and a filtering method to process the measurements of inter-satellite ranges. The onboard data processing unit is the main module of autonomous navigation systems. In this paper, the two main factors that affect the accuracy of autonomous orbit determination for a navigation constellation are discussed first, and then a distributed onboard algorithm for autonomous orbit determination of navigation satellites is proposed. This method is based on a long-term ephemeris prediction and is suitable for the satellite hardware capability. The main feature of this method is that both the distributed computing method and an onboard analytical state transition matrix are used to process inter-satellite range measurements. One of the main advantages of this approach is high-speed computing since the amount of calculations needed is significantly less than that of the centralised computing method and those distributed methods that need to use an onboard numerical integrator. Another advantage of this approach is that the use of the onboard analytical state transition matrix algorithm can save a great amount of resources for both ground-to-satellite data transmissions and data storage units in satellites’ hardware. This could result in substantial cost reduction for space missions. Finally, a simulation method used for testing the proposed algorithm is presented. Results of tests over a period of 90 days show that the user range error of autonomous orbit determination derived from the proposed method is less than three metres.

The Design of a Wavelet-Based Neural Network Adaptive Filter

2013 ◽  
Vol 860-863 ◽  
pp. 2791-2795
Author(s):  
Qian Xiao ◽  
Yu Shan Jiang ◽  
Ru Zheng Cui
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Real Time ◽  
Adaptive Filter ◽  
Adaptive Algorithm ◽  
Distributed Storage ◽  
Hopfield Neural Network ◽  
Lms Algorithm ◽  
The Neural Network ◽  
Simulation Results

Aiming at the large calculation workload of adaptive algorithm in adaptive filter based on wavelet transform, affecting the filtering speed, a wavelet-based neural network adaptive filter is constructed in this paper. Since the neural network has the ability of distributed storage and fast self-evolution, use Hopfield neural network to implement adaptive filter LMS algorithm in this filter so as to improve the speed of operation. The simulation results prove that, the new filter can achieve rapid real-time denoising.

scholarly journals Network-based Observability and Controllability Analysis of Dynamical Systems: the NOCAD toolbox

F1000Research ◽  
2019 ◽  
Vol 8 ◽  
pp. 646
Author(s):  
Dániel Leitold ◽  
Ágnes Vathy-Fogarassy ◽  
János Abonyi
Keyword(s):  
Neural Network ◽  
Caenorhabditis Elegans ◽  
Dynamical Systems ◽  
Network Science ◽  
Sensor Placement ◽  
Life Sciences ◽  
The Neural Network ◽  
Structural Controllability ◽  
Controllability And Observability

The network science-based determination of driver nodes and sensor placement has become increasingly popular in the field of dynamical systems over the last decade. In this paper, the applicability of the methodology in the field of life sciences is introduced through the analysis of the neural network of Caenorhabditis elegans. Simultaneously, an Octave and MATLAB-compatible NOCAD toolbox is proposed that provides a set of methods to automatically generate the relevant structural controllability and observability associated measures for linear or linearised systems and compare the different sensor placement methods.

Modeling of Uncertain Nonlinear System With Z-Numbers

2021 ◽  
pp. 290-314
Author(s):  
Raheleh Jafari ◽  
Sina Razvarz ◽  
Alexander Gegov ◽  
Satyam Paul
Keyword(s):  
Neural Network ◽  
Nonlinear System ◽  
Nonlinear Systems ◽  
Network Approach ◽  
Uncertain Nonlinear System ◽  
Neural Network Approach ◽  
The Neural Network ◽  
Simulation Results ◽  
Fuzzy Equations

In order to model the fuzzy nonlinear systems, fuzzy equations with Z-number coefficients are used in this chapter. The modeling of fuzzy nonlinear systems is to obtain the Z-number coefficients of fuzzy equations. In this work, the neural network approach is used for finding the coefficients of fuzzy equations. Some examples with applications in mechanics are given. The simulation results demonstrate that the proposed neural network is effective for obtaining the Z-number coefficients of fuzzy equations.

Synthesis, spectral description, and lipophilicity parameters determination of phenylcarbamic acid derivatives with integrated N-phenylpiperazine moiety in the structure

2006 ◽  
Vol 60 (1) ◽  
Author(s):  
I. Malík ◽  
E. Sedlárová ◽  
J. Csöllei ◽  
F. Andriamainty ◽  
P. Kurfürst ◽  
...  
Keyword(s):  
Neural Network ◽  
Spectral Data ◽  
1H Nmr ◽  
Elemental Analysis ◽  
Computer Programs ◽  
Mass Spectral Data ◽  
Mass Spectral ◽  
The Neural Network ◽  
Lipophilicity Parameters

AbstractThe phenylcarbamic acid derivatives with N-phenylpiperazine moiety in the molecule have been prepared. The structure has been confirmed by elemental analysis, IR, 1H NMR, and mass spectral data. For the prepared set of the compounds the lipophilicity parameters have been determined. The experimentally obtained lipophilicity parameters have been correlated with theoretical entries obtained by different computer programs based on the neural network and fragmental methods.

End Effect Processing for Empirical Mode Decomposition Using Fuzzy Inductive Reasoning

2011 ◽  
Vol 55-57 ◽  
pp. 407-412 ◽  
Author(s):  
Ye Yuan ◽  
Zhong Kai Yang ◽  
Qing Fu Li
Keyword(s):  
Neural Network ◽  
Empirical Mode Decomposition ◽  
Input Signal ◽  
Inductive Reasoning ◽  
Qualitative Modeling ◽  
End Effect ◽  
Predictive Capability ◽  
Mode Decomposition ◽  
The Neural Network ◽  
Simulation Results

This paper focuses on the end effect problem of the empirical mode decomposition (EMD) algorithm, which results in a serious distortion in the EMD sifting process. A new method based on fuzzy inductive reasoning (FIR) is proposed to overcome the end effect. Fuzzy inductive reasoning method has simple inferring rules and strong predictive capability. The fuzzy inductive reasoning based method uses the sequence near the end as the input signal of fuzzy inductive reasoning model. This predictive value can be obtained after fuzzification, qualitative modeling ,qualitative simulation and debluring. The simulation results have shown that the fuzzy inductive reasoning based method has equivalent performance to the neural network based method.

scholarly journals A Compact Gradient Based Neural Network for Capon Spectral Estimation

2020 ◽  
Vol 7 ◽  
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Spectral Estimation ◽  
Original Method ◽  
Vector Product ◽  
Neural Network Architecture ◽  
Computational Burden ◽  
The Neural Network ◽  
Gradient Based ◽  
Simulation Results

This paper describes the use of a novel gradient based recurrent neural network to perform Capon spectral estimation. Nowadays, in the fastest algorithm proposed by Marple et al., the computational burden still remains significant in the calculation of the autoregressive (AR) Parameters. In this paper we propose to use a gradient based neural network to compute the AR parameters by solving the Yule-Walker equations. Furthermore, to reduce the complexity of the neural network architecture, the weights matrixinputs vector product is performed efficiently using the fast Fourier transform. Simulation results show that proposed neural network and its simplified architecture lead to the same results as the original method which prove the correctness of the proposed scheme.

Sign in / Sign up

Export Citation Format

Share Document