The next-generation gravity satellite mission equipped with the Cold Atom Interferometry(CAI)gradiometer has great potential for the Earth's gravity field estimation.Deploying a CAI gradiometer on the Chinese Tian...The next-generation gravity satellite mission equipped with the Cold Atom Interferometry(CAI)gradiometer has great potential for the Earth's gravity field estimation.Deploying a CAI gradiometer on the Chinese Tiangong Space Station launched for long-term Earth science research not only reduces the cost compared to a dual-satellite constellation but also enhances interdisciplinary collaboration in the Earth's gravity field detection.In this study,we conducted gravity gradient-based simulations to assess the contribution of deploying a CAI gradiometer on the Tiangong Space Station to collaboratively observe the Earth's gravity field with a polar-orbit gravity satellite.The simulation results demonstrate that whether utilizing V_(yy) component,three diagonal components or full components,the derived gravity field models show significant improvements within 100 degree and above 200 degree after incorporating Tiangong Space Station.In particular,the gravity field solution recovered from three diagonal components achieves the best accuracy.In the case of using diagonal components,the collaboration observation scheme effectively reduced the cumulative geoid height error by approximately 5.3 cm(300 d/o).In the spatial domain,the incorporation of the Tiangong Space Station primarily impacts the estimated gravity field within the orbital coverage area of the space station,and this effect is particularly pronounced when just employing V_(yy) component.However,due to the limitation of angular velocity observation inaccuracy associated with the CAI gradiometer in nadir mode,there is no substantial accuracy improvement observed above 200 degree when adding gradient components.展开更多
The popularity of quadrotor Unmanned Aerial Vehicles(UAVs)stems from their simple propulsion systems and structural design.However,their complex and nonlinear dynamic behavior presents a significant challenge for cont...The popularity of quadrotor Unmanned Aerial Vehicles(UAVs)stems from their simple propulsion systems and structural design.However,their complex and nonlinear dynamic behavior presents a significant challenge for control,necessitating sophisticated algorithms to ensure stability and accuracy in flight.Various strategies have been explored by researchers and control engineers,with learning-based methods like reinforcement learning,deep learning,and neural networks showing promise in enhancing the robustness and adaptability of quadrotor control systems.This paper investigates a Reinforcement Learning(RL)approach for both high and low-level quadrotor control systems,focusing on attitude stabilization and position tracking tasks.A novel reward function and actor-critic network structures are designed to stimulate high-order observable states,improving the agent’s understanding of the quadrotor’s dynamics and environmental constraints.To address the challenge of RL hyper-parameter tuning,a new framework is introduced that combines Simulated Annealing(SA)with a reinforcement learning algorithm,specifically Simulated Annealing-Twin Delayed Deep Deterministic Policy Gradient(SA-TD3).This approach is evaluated for path-following and stabilization tasks through comparative assessments with two commonly used control methods:Backstepping and Sliding Mode Control(SMC).While the implementation of the well-trained agents exhibited unexpected behavior during real-world testing,a reduced neural network used for altitude control was successfully implemented on a Parrot Mambo mini drone.The results showcase the potential of the proposed SA-TD3 framework for real-world applications,demonstrating improved stability and precision across various test scenarios and highlighting its feasibility for practical deployment.展开更多
A direct numerical simulation （DNS） on an oblique shock wave with an incident angle of 33.2° impinging on a Mach 2.25 supersonic turbulent boundary layer is performed. The numerical results are confirmed to be ...A direct numerical simulation （DNS） on an oblique shock wave with an incident angle of 33.2° impinging on a Mach 2.25 supersonic turbulent boundary layer is performed. The numerical results are confirmed to be of high accuracy by comparison with the reference data. Particular efforts have been made on the investigation of the near-wall behaviors in the interaction region, where the pressure gradient is so significant that a certain separation zone emerges. It is found that, the traditional linear and loga- rithmic laws, which describe the mean-velocity profiles in the viscous and meso sublayers, respectively, cease to be valid in the neighborhood of the interaction region, and two new laws of the wall are proposed by elevating the pressure gradient to the leading order. The new laws are inspired by the analysis on the incompressible separation flows, while the compressibility is additionally taken into account. It is verified by the DNS results that the new laws are adequate to reproduce the mean-velocity profiles both inside and outside the interaction region. Moreover, the normalization adopted in the new laws is able to regularize the Reynolds stress into an almost universal distribution even with a salient adverse pressure gradient （APG）.展开更多
基金National Key R&D Program of China(2021YFB3900101)the National Natural Science Foundation of China(42174099 and 42192532)It is also partly supported by the Fundamental Research Funds for the Central Universities.
文摘The next-generation gravity satellite mission equipped with the Cold Atom Interferometry(CAI)gradiometer has great potential for the Earth's gravity field estimation.Deploying a CAI gradiometer on the Chinese Tiangong Space Station launched for long-term Earth science research not only reduces the cost compared to a dual-satellite constellation but also enhances interdisciplinary collaboration in the Earth's gravity field detection.In this study,we conducted gravity gradient-based simulations to assess the contribution of deploying a CAI gradiometer on the Tiangong Space Station to collaboratively observe the Earth's gravity field with a polar-orbit gravity satellite.The simulation results demonstrate that whether utilizing V_(yy) component,three diagonal components or full components,the derived gravity field models show significant improvements within 100 degree and above 200 degree after incorporating Tiangong Space Station.In particular,the gravity field solution recovered from three diagonal components achieves the best accuracy.In the case of using diagonal components,the collaboration observation scheme effectively reduced the cumulative geoid height error by approximately 5.3 cm(300 d/o).In the spatial domain,the incorporation of the Tiangong Space Station primarily impacts the estimated gravity field within the orbital coverage area of the space station,and this effect is particularly pronounced when just employing V_(yy) component.However,due to the limitation of angular velocity observation inaccuracy associated with the CAI gradiometer in nadir mode,there is no substantial accuracy improvement observed above 200 degree when adding gradient components.
基金supported by Princess Nourah Bint Abdulrahman University Researchers Supporting Project number(PNURSP2024R135)Princess Nourah Bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘The popularity of quadrotor Unmanned Aerial Vehicles(UAVs)stems from their simple propulsion systems and structural design.However,their complex and nonlinear dynamic behavior presents a significant challenge for control,necessitating sophisticated algorithms to ensure stability and accuracy in flight.Various strategies have been explored by researchers and control engineers,with learning-based methods like reinforcement learning,deep learning,and neural networks showing promise in enhancing the robustness and adaptability of quadrotor control systems.This paper investigates a Reinforcement Learning(RL)approach for both high and low-level quadrotor control systems,focusing on attitude stabilization and position tracking tasks.A novel reward function and actor-critic network structures are designed to stimulate high-order observable states,improving the agent’s understanding of the quadrotor’s dynamics and environmental constraints.To address the challenge of RL hyper-parameter tuning,a new framework is introduced that combines Simulated Annealing(SA)with a reinforcement learning algorithm,specifically Simulated Annealing-Twin Delayed Deep Deterministic Policy Gradient(SA-TD3).This approach is evaluated for path-following and stabilization tasks through comparative assessments with two commonly used control methods:Backstepping and Sliding Mode Control(SMC).While the implementation of the well-trained agents exhibited unexpected behavior during real-world testing,a reduced neural network used for altitude control was successfully implemented on a Parrot Mambo mini drone.The results showcase the potential of the proposed SA-TD3 framework for real-world applications,demonstrating improved stability and precision across various test scenarios and highlighting its feasibility for practical deployment.
基金Project supported by the National Natural Science Foundation of China(Nos.11472189 and11332007)
文摘A direct numerical simulation （DNS） on an oblique shock wave with an incident angle of 33.2° impinging on a Mach 2.25 supersonic turbulent boundary layer is performed. The numerical results are confirmed to be of high accuracy by comparison with the reference data. Particular efforts have been made on the investigation of the near-wall behaviors in the interaction region, where the pressure gradient is so significant that a certain separation zone emerges. It is found that, the traditional linear and loga- rithmic laws, which describe the mean-velocity profiles in the viscous and meso sublayers, respectively, cease to be valid in the neighborhood of the interaction region, and two new laws of the wall are proposed by elevating the pressure gradient to the leading order. The new laws are inspired by the analysis on the incompressible separation flows, while the compressibility is additionally taken into account. It is verified by the DNS results that the new laws are adequate to reproduce the mean-velocity profiles both inside and outside the interaction region. Moreover, the normalization adopted in the new laws is able to regularize the Reynolds stress into an almost universal distribution even with a salient adverse pressure gradient （APG）.
