Transactions of Nanjing University of Aeronautics & Astronautics
Issue 4,2022 Table of Contents
2022(4):379-388.
DOI: 10.16356/j.1005-1120.2022.04.001
Abstract:
This paper proposes an intelligent low-thrust orbit phasing control method for multiple spacecraft by simultaneously considering fuel optimization and collision avoidance. Firstly， the minimum-fuel orbit phasing control database is generated by the indirect method associated with the homotopy technique. Then， a deep network representing the minimum-fuel solution is trained. To avoid collision for multiple spacecraft， an artificial potential function is introduced in the collision-avoidance controller. Finally， an intelligent orbit phasing control method by combining the minimum-fuel neural network controller and the collision-avoidance controller is proposed. Numerical results show that the proposed intelligent orbit phasing control is valid for the multi-satellite constellation initialization without collision.
This paper proposes an intelligent low-thrust orbit phasing control method for multiple spacecraft by simultaneously considering fuel optimization and collision avoidance. Firstly， the minimum-fuel orbit phasing control database is generated by the indirect method associated with the homotopy technique. Then， a deep network representing the minimum-fuel solution is trained. To avoid collision for multiple spacecraft， an artificial potential function is introduced in the collision-avoidance controller. Finally， an intelligent orbit phasing control method by combining the minimum-fuel neural network controller and the collision-avoidance controller is proposed. Numerical results show that the proposed intelligent orbit phasing control is valid for the multi-satellite constellation initialization without collision.
2022(4):389-399.
DOI: 10.16356/j.1005-1120.2022.04.002
Abstract:
This paper proposes a self-tuning iterative learning control method for the attitude control of a flexible solar power satellite， which is simplified as an Euler-Bernoulli beam moving in space. An orbit-attitude-structure coupled dynamic model is established using absolute nodal coordinate formulation， and the attitude control is performed using two control moment gyros. In order to improve control accuracy of the classic proportional-derivative control method， a switched iterative learning control method is presented using the control moments of the previous periods as feedforward control moments. Although the iterative learning control is a model-free method， the parameters of the controller must be selected manually. This would be undesirable for complicated systems with multiple control parameters. Thus， a self-tuning method is proposed using fuzzy logic. The control frequency of the controller is adjusted according to the averaged control error in one control period. Simulation results show that the proposed controller increases the control accuracy greatly and reduces the influence of measurement noise. Moreover， the control frequency is automatically adjusted to a suitable value.
This paper proposes a self-tuning iterative learning control method for the attitude control of a flexible solar power satellite， which is simplified as an Euler-Bernoulli beam moving in space. An orbit-attitude-structure coupled dynamic model is established using absolute nodal coordinate formulation， and the attitude control is performed using two control moment gyros. In order to improve control accuracy of the classic proportional-derivative control method， a switched iterative learning control method is presented using the control moments of the previous periods as feedforward control moments. Although the iterative learning control is a model-free method， the parameters of the controller must be selected manually. This would be undesirable for complicated systems with multiple control parameters. Thus， a self-tuning method is proposed using fuzzy logic. The control frequency of the controller is adjusted according to the averaged control error in one control period. Simulation results show that the proposed controller increases the control accuracy greatly and reduces the influence of measurement noise. Moreover， the control frequency is automatically adjusted to a suitable value.
2022(4):400-414.
DOI: 10.16356/j.1005-1120.2022.04.003
Abstract:
Aiming at the problem of relative navigation for non-cooperative rendezvous of spacecraft， this paper proposes a new angles-only navigation architecture using non-linear dynamics method. This method does not solve the problem of poor observability of angles-only navigation through orbital or attitude maneuvering， but improves the observability of angles-only navigation through capturing the non-linearity of the system in the evolution of relative motion. First， three relative dynamics models and their corresponding line-of-sight （LoS） measurement equations are introduced， including the rectilinear state relative dynamics model， the curvilinear state relative dynamics model， and the relative orbital elements （ROE） state relative dynamics model. Then， an observability analysis theory based on the Gramian matrix is introduced to determine which relative dynamics model could maximize the observability of angles-only navigation. Next， an adaptive extended Kalman filtering scheme is proposed to solve the problem that the angles-only navigation filter using the non-linear dynamics method is sensitive to measurement noises. Finally， the performances of the proposed angles-only navigation architecture are tested by means of numerical simulations， which demonstrates that the angles-only navigation filtering scheme without orbital or attitude maneuvering is completely feasible through improving the modeling of the relative dynamics and LoS measurement equations.
Aiming at the problem of relative navigation for non-cooperative rendezvous of spacecraft， this paper proposes a new angles-only navigation architecture using non-linear dynamics method. This method does not solve the problem of poor observability of angles-only navigation through orbital or attitude maneuvering， but improves the observability of angles-only navigation through capturing the non-linearity of the system in the evolution of relative motion. First， three relative dynamics models and their corresponding line-of-sight （LoS） measurement equations are introduced， including the rectilinear state relative dynamics model， the curvilinear state relative dynamics model， and the relative orbital elements （ROE） state relative dynamics model. Then， an observability analysis theory based on the Gramian matrix is introduced to determine which relative dynamics model could maximize the observability of angles-only navigation. Next， an adaptive extended Kalman filtering scheme is proposed to solve the problem that the angles-only navigation filter using the non-linear dynamics method is sensitive to measurement noises. Finally， the performances of the proposed angles-only navigation architecture are tested by means of numerical simulations， which demonstrates that the angles-only navigation filtering scheme without orbital or attitude maneuvering is completely feasible through improving the modeling of the relative dynamics and LoS measurement equations.
2022(4):415-424.
DOI: 10.16356/j.1005-1120.2022.04.004
Abstract:
A distributed relative navigation approach via inter-satellite sensing and communication for satellite clusters is proposed. The inter-satellite link （ISL） is used for ranging and exchanging data for the relative navigation， which can improve the autonomy of the satellite cluster. The ISL topology design problem is formulated as a multi-objective optimization problem where the energy consumption and the navigation performance are considered. Further， the relative navigation is performed in a distributed fashion， where each satellite in the cluster makes observations and communicates with its neighbors via the ISL locally such that the transmission consumption and the computational complexity for the navigation are reduced. The ISL topology optimization problem is solved via the NSGA-Ⅱ algorithm， and the consensus Kalman filter is used for the distributed relative navigation. The proposed approach is flexible to varying tasks， with satellites joining or leaving the cluster anytime， and is robust to the failure of an individual satellite. Numerical simulations are presented to verify the feasibility of the proposed approach.
A distributed relative navigation approach via inter-satellite sensing and communication for satellite clusters is proposed. The inter-satellite link （ISL） is used for ranging and exchanging data for the relative navigation， which can improve the autonomy of the satellite cluster. The ISL topology design problem is formulated as a multi-objective optimization problem where the energy consumption and the navigation performance are considered. Further， the relative navigation is performed in a distributed fashion， where each satellite in the cluster makes observations and communicates with its neighbors via the ISL locally such that the transmission consumption and the computational complexity for the navigation are reduced. The ISL topology optimization problem is solved via the NSGA-Ⅱ algorithm， and the consensus Kalman filter is used for the distributed relative navigation. The proposed approach is flexible to varying tasks， with satellites joining or leaving the cluster anytime， and is robust to the failure of an individual satellite. Numerical simulations are presented to verify the feasibility of the proposed approach.
2022(4):425-433.
DOI: 10.16356/j.1005-1120.2022.04.005
Abstract:
In order to improve the recognition accuracy of similar weather scenarios（SWSs） in terminal area， a recognition model for SWS based on contrastive learning （SWS-CL） is proposed. Firstly， a data augmentation method is designed to improve the number and quality of weather scenarios samples according to the characteristics of convective weather images. Secondly， in the pre-trained recognition model of SWS-CL， a loss function is formulated to minimize the distance between the anchor and positive samples， and maximize the distance between the anchor and the negative samples in the latent space. Finally， the pre-trained SWS-CL model is fine-tuned with labeled samples to improve the recognition accuracy of SWS. The comparative experiments on the weather images of Guangzhou terminal area show that the proposed data augmentation method can effectively improve the quality of weather image dataset， and the proposed SWS-CL model can achieve satisfactory recognition accuracy. It is also verified that the fine-tuned SWS-CL model has obvious advantages in datasets with sparse labels.
In order to improve the recognition accuracy of similar weather scenarios（SWSs） in terminal area， a recognition model for SWS based on contrastive learning （SWS-CL） is proposed. Firstly， a data augmentation method is designed to improve the number and quality of weather scenarios samples according to the characteristics of convective weather images. Secondly， in the pre-trained recognition model of SWS-CL， a loss function is formulated to minimize the distance between the anchor and positive samples， and maximize the distance between the anchor and the negative samples in the latent space. Finally， the pre-trained SWS-CL model is fine-tuned with labeled samples to improve the recognition accuracy of SWS. The comparative experiments on the weather images of Guangzhou terminal area show that the proposed data augmentation method can effectively improve the quality of weather image dataset， and the proposed SWS-CL model can achieve satisfactory recognition accuracy. It is also verified that the fine-tuned SWS-CL model has obvious advantages in datasets with sparse labels.
2022(4):434-449.
DOI: 10.16356/j.1005-1120.2022.04.006
Abstract:
Target detection in low light background is one of the main tasks of night patrol robots for airport terminal. However， if some algorithms can run on a robot platform with limited computing resources， it is difficult for these algorithms to ensure the detection accuracy of human body in the airport terminal. A novel thermal infrared salient human detection model combined with thermal features called TFSHD is proposed. The TFSHD model is still based on U-Net， but the decoder module structure and model lightweight have been redesigned. In order to improve the detection accuracy of the algorithm in complex scenes， a fusion module composed of thermal branch and saliency branch is added to the decoder of the TFSHD model. Furthermore， a predictive loss function that is more sensitive to high temperature regions of the image is designed. Additionally， for the sake of reducing the computing resource requirements of the algorithm， a model lightweight scheme that includes simplifying the encoder network structure and controlling the number of decoder channels is adopted. The experimental results on four data sets show that the proposed method can not only ensure high detection accuracy and robustness of the algorithm， but also meet the needs of real-time detection of patrol robots with detection speed above 40 f/s.
Target detection in low light background is one of the main tasks of night patrol robots for airport terminal. However， if some algorithms can run on a robot platform with limited computing resources， it is difficult for these algorithms to ensure the detection accuracy of human body in the airport terminal. A novel thermal infrared salient human detection model combined with thermal features called TFSHD is proposed. The TFSHD model is still based on U-Net， but the decoder module structure and model lightweight have been redesigned. In order to improve the detection accuracy of the algorithm in complex scenes， a fusion module composed of thermal branch and saliency branch is added to the decoder of the TFSHD model. Furthermore， a predictive loss function that is more sensitive to high temperature regions of the image is designed. Additionally， for the sake of reducing the computing resource requirements of the algorithm， a model lightweight scheme that includes simplifying the encoder network structure and controlling the number of decoder channels is adopted. The experimental results on four data sets show that the proposed method can not only ensure high detection accuracy and robustness of the algorithm， but also meet the needs of real-time detection of patrol robots with detection speed above 40 f/s.
2022(4):450-466.
DOI: 10.16356/j.1005-1120.2022.04.007
Abstract:
A graphics processing unit （GPU）-accelerated discontinuous Galerkin （DG） method is presented for solving two-dimensional laminar flows. The DG method is ported from central processing unit to GPU in a way of achieving GPU speedup through programming under the compute unified device architecture （CUDA） model. The CUDA kernel subroutines are designed to meet with the requirement of high order computing of DG method. The corresponding data structures are constructed in component-wised manners and the thread hierarchy is manipulated in cell-wised or edge-wised manners associated with related integrals involved in solving laminar Navier-Stokes equations， in which the inviscid and viscous flux terms are computed by the local lax-Friedrichs scheme and the second scheme of Bassi & Rebay， respectively. A strong stability preserving Runge-Kutta scheme is then used for time marching of numerical solutions. The resulting GPU-accelerated DG method is first validated by the traditional Couette flow problems with different mesh sizes associated with different orders of approximation， which shows that the orders of convergence， as expected， can be achieved. The numerical simulations of the typical flows over a circular cylinder or a NACA 0012 airfoil are then carried out， and the results are further compared with the analytical solutions or available experimental and numerical values reported in the literature， as well as with a performance analysis of the developed code in terms of GPU speedups. This shows that the costs of computing time of the presented test cases are significantly reduced without losing accuracy， while impressive speedups up to 69.7 times are achieved by the present method in comparison to its CPU counterpart.
A graphics processing unit （GPU）-accelerated discontinuous Galerkin （DG） method is presented for solving two-dimensional laminar flows. The DG method is ported from central processing unit to GPU in a way of achieving GPU speedup through programming under the compute unified device architecture （CUDA） model. The CUDA kernel subroutines are designed to meet with the requirement of high order computing of DG method. The corresponding data structures are constructed in component-wised manners and the thread hierarchy is manipulated in cell-wised or edge-wised manners associated with related integrals involved in solving laminar Navier-Stokes equations， in which the inviscid and viscous flux terms are computed by the local lax-Friedrichs scheme and the second scheme of Bassi & Rebay， respectively. A strong stability preserving Runge-Kutta scheme is then used for time marching of numerical solutions. The resulting GPU-accelerated DG method is first validated by the traditional Couette flow problems with different mesh sizes associated with different orders of approximation， which shows that the orders of convergence， as expected， can be achieved. The numerical simulations of the typical flows over a circular cylinder or a NACA 0012 airfoil are then carried out， and the results are further compared with the analytical solutions or available experimental and numerical values reported in the literature， as well as with a performance analysis of the developed code in terms of GPU speedups. This shows that the costs of computing time of the presented test cases are significantly reduced without losing accuracy， while impressive speedups up to 69.7 times are achieved by the present method in comparison to its CPU counterpart.
9 Hybrid Fault Diagnosis and Isolation for Component and Sensor of APU in a Distributed Control System
2022(4):467-481.
DOI: 10.16356/j.1005-1120.2022.04.008
Abstract:
This paper addresses the gas path component and sensor fault diagnosis and isolation （FDI） for the auxiliary power unit （APU）. A nonlinear dynamic model and a distributed state estimator are combined for the distributed control system. The distributed extended Kalman filter （DEKF） is served as a state estimator， which is utilized to estimate the gas path components’ flow capacity. The DEKF includes one main filter and five sub-filter groups related to five sensors of APU and each sub-filter yields local state flow capacity. The main filter collects and fuses the local state information， and then the state estimations are feedback to the sub-filters. The packet loss model is introduced in the DEKF algorithm in the APU distributed control architecture. FDI strategy with a performance index named weight sum of squared residuals （WSSR） is designed and used to identify the APU sensor fault by removing one sub-filter each time. The very sensor fault occurs as its performance index WSSR is different from the remaining sub-filter combinations. And the estimated value of the soft redundancy replaces the fault sensor measurement to isolate the fault measurement. It is worth noting that the proposed approach serves for not only the sensor failure but also the hybrid fault issue of APU gas path components and sensors. The simulation and comparison are systematically carried out by using the APU test data， and the superiority of the proposed methodology is verified.
This paper addresses the gas path component and sensor fault diagnosis and isolation （FDI） for the auxiliary power unit （APU）. A nonlinear dynamic model and a distributed state estimator are combined for the distributed control system. The distributed extended Kalman filter （DEKF） is served as a state estimator， which is utilized to estimate the gas path components’ flow capacity. The DEKF includes one main filter and five sub-filter groups related to five sensors of APU and each sub-filter yields local state flow capacity. The main filter collects and fuses the local state information， and then the state estimations are feedback to the sub-filters. The packet loss model is introduced in the DEKF algorithm in the APU distributed control architecture. FDI strategy with a performance index named weight sum of squared residuals （WSSR） is designed and used to identify the APU sensor fault by removing one sub-filter each time. The very sensor fault occurs as its performance index WSSR is different from the remaining sub-filter combinations. And the estimated value of the soft redundancy replaces the fault sensor measurement to isolate the fault measurement. It is worth noting that the proposed approach serves for not only the sensor failure but also the hybrid fault issue of APU gas path components and sensors. The simulation and comparison are systematically carried out by using the APU test data， and the superiority of the proposed methodology is verified.
10 Application of WSGSA Model in Predicting Temperature and Soot in C2H4/Air Turbulent Diffusion Flame
2022(4):482-492.
DOI: 10.16356/j.1005-1120.2022.04.009
Abstract:
Soot， a product of insufficient combustion， is usually in the form of aggregate. The multi-scattering of soot fractal aggregates has been proved to play an important role in studying the soot radiative properties， which is rarely considered in predicting the radiative heat transfer in combustion flame. In the present study， based on the weighted sum of gray soot fractal aggregate （WSGSA） model，which is used to predict the temperature field and soot aggregates in turbulent diffusion flame， the flame temperature distribution and soot volume fraction distribution under the conditions of the model without considering radiation， the default radiation model in Fluent software and the WSGSA model are calculated respectively. The results show that the flame temperature will be seriously overestimated without considering radiation and the maximum relative discrepancy of flame centerline temperature is about 64.5%. The accuracy will be improved by the default radiation model in the Fluent software， but the flame temperature is still overestimated and the maximum relative discrepancy of flame centerline temperature is about 42.1%. However， more satisfactory results can be obtained by the WSGSA model， and the maximum relative discrepancy of flame centerline temperature is no more than 15.3%. Similar conclusions can also be obtained in studying the temperature distribution along different flame heights. Moreover， the soot volume fraction can be predicted more accurately with the application of the WSGSA model. Both without considering radiation and using the default radiation model in the Fluent software will result in the underestimating of soot volume fraction. All the results reveal that the WSGSA model can be used to predict the temperature and soot aggregates in the C2H4/air turbulent diffusion flame.
Soot， a product of insufficient combustion， is usually in the form of aggregate. The multi-scattering of soot fractal aggregates has been proved to play an important role in studying the soot radiative properties， which is rarely considered in predicting the radiative heat transfer in combustion flame. In the present study， based on the weighted sum of gray soot fractal aggregate （WSGSA） model，which is used to predict the temperature field and soot aggregates in turbulent diffusion flame， the flame temperature distribution and soot volume fraction distribution under the conditions of the model without considering radiation， the default radiation model in Fluent software and the WSGSA model are calculated respectively. The results show that the flame temperature will be seriously overestimated without considering radiation and the maximum relative discrepancy of flame centerline temperature is about 64.5%. The accuracy will be improved by the default radiation model in the Fluent software， but the flame temperature is still overestimated and the maximum relative discrepancy of flame centerline temperature is about 42.1%. However， more satisfactory results can be obtained by the WSGSA model， and the maximum relative discrepancy of flame centerline temperature is no more than 15.3%. Similar conclusions can also be obtained in studying the temperature distribution along different flame heights. Moreover， the soot volume fraction can be predicted more accurately with the application of the WSGSA model. Both without considering radiation and using the default radiation model in the Fluent software will result in the underestimating of soot volume fraction. All the results reveal that the WSGSA model can be used to predict the temperature and soot aggregates in the C2H4/air turbulent diffusion flame.
11 A Current Mode Low-Noise Gm-C Filter with a Cut-Off Frequency of 5 GHz in Telecommunication System
2022(4):493-498.
DOI: 10.16356/j.1005-1120.2022.04.010
Abstract:
The high linearity low-noise filter is an indispensable key circuit in the communication system. Based on the structure of current-reuse source-degradation operational transconductance amplifier （OTA）， a 5 GHz current-mode low-noise Gm-C filter suitable for high-speed communication systems is proposed. Thanks to the proposed current mode structure and the OTA’s high-power efficiency and high linearity， the filter obtains good noise and high linearity performance with very low power consumption. The filter is designed in standard 65 nm CMOS technology and occupies a core area of 0.06 mm2. The simulation results show that the operating bandwidth is 5 GHz， the IIP3 is 35 dBm， and the power consumption is only 3.2 mW.
The high linearity low-noise filter is an indispensable key circuit in the communication system. Based on the structure of current-reuse source-degradation operational transconductance amplifier （OTA）， a 5 GHz current-mode low-noise Gm-C filter suitable for high-speed communication systems is proposed. Thanks to the proposed current mode structure and the OTA’s high-power efficiency and high linearity， the filter obtains good noise and high linearity performance with very low power consumption. The filter is designed in standard 65 nm CMOS technology and occupies a core area of 0.06 mm2. The simulation results show that the operating bandwidth is 5 GHz， the IIP3 is 35 dBm， and the power consumption is only 3.2 mW.
2022(4):499-506.
DOI: 10.16356/j.1005-1120.2022.04.011
Abstract:
This paper presents fractional generalized canonical transformations for fractional Birkhoffian systems within Caputo derivatives. Firstly， based on fractional Pfaff-Birkhoff principle within Caputo derivatives， fractional Birkhoff’s equations are derived and the basic identity of constructing generalized canonical transformations is proposed. Secondly， according to the fact that the generating functions contain new and old variables， four kinds of generating functions of the fractional Birkhoffian system are proposed， and four basic forms of fractional generalized canonical transformations are deduced. Then， fractional canonical transformations for fractional Hamiltonian system are given. Some interesting examples are finally listed.
This paper presents fractional generalized canonical transformations for fractional Birkhoffian systems within Caputo derivatives. Firstly， based on fractional Pfaff-Birkhoff principle within Caputo derivatives， fractional Birkhoff’s equations are derived and the basic identity of constructing generalized canonical transformations is proposed. Secondly， according to the fact that the generating functions contain new and old variables， four kinds of generating functions of the fractional Birkhoffian system are proposed， and four basic forms of fractional generalized canonical transformations are deduced. Then， fractional canonical transformations for fractional Hamiltonian system are given. Some interesting examples are finally listed.
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