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State Transition Tensors for Continuous-Thrust Control of Three-Body Relative Motion

JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2023年第8期46卷 1610-1619页

作者： Kulik, Jackson Clark, William Savransky, Dmitry Cornell Univ Ctr Appl Math Ithaca NY 14850 USA Cornell Univ Math Ithaca NY 14850 USA Cornell Univ Mech & Aerosp Engn Ithaca NY 14850 USA

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Fusion Detection via Distance-Decay Intersection over Union and Weighted Dempster-Shafer Evidence Theory

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JOURNAL OF AEROSPACE INFORMATION SYSTEMS 2023年第3期20卷 114-125页

作者： Fang, Qingyun Wang, Zhaokui Tsinghua Univ Dept Aeronaut & Astronaut Engn Beijing 100084 Peoples R China

In recent years, increasing attention has been paid to object detection in remote sensing imagery. However, traditional optical detection is highly susceptible to illumination and weather anomalies. It is a challenge to effectively utilize the cross-modality information from multisource remote sensing images, especially from optical and synthetic aperture radar images, to achieve all-day and all-weather detection with high accuracy and speed. Toward this end, a fast multisource fusion detection framework FusionDet is proposed in the current paper. A novel distance-decay intersection over union is employed to encode the shape properties of the targets with scale invariance. Therefore, the same target in multisource images can be paired accurately. Furthermore, the weighted Dempster-Shafer evidence theory is utilized to combine the optical and synthetic aperture radar detection, which overcomes the drawback in a feature-level fusion that requires a large amount of paired data. In addition, the paired optical and synthetic aperture radar images are taken to demonstrate our fusion algorithm. Considerable experiments and ablation studies on multiple datasets show that our method is generally effective and achieves state-of-the-art detection performance.

关键词： Synthetic Aperture Radar Remote Sensing and Applications Ablation Satellite Formation Optical Communication Constellations computing and informatics Artificial Neural Network Earth Observation Satellite Probability Distribution

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Fast and Robust Computation of Optimal Rotor Designs Using Blade Element Momentum Theory

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AIAA JOURNAL 2023年第9期61卷 4096-4111页

作者： Ruh, Marius L. Hwang, John T. Univ Calif San Diego Dept Mech & Aerosp Engn La Jolla CA 92093 USA

Recent years have seen a renewed interest in rotary-wing aircraft, due to the emergence of urban air mobility. In the conceptual design of such aircraft, fast estimation of rotor performance is important. A challenge is that rotor performance models require a detailed description of the blade geometry, which is not always available at this stage. We have developed a new, robust method for rapidly computing optimal rotor designs, which is derived from blade element momentum theory. In this paper, we derive the rotor design method and prove its convergence for all reasonable model inputs. We verify the design method by comparing the optimal rotor designs it computes to those found by a nonlinear programming algorithm. Thousands of numerical experiments yield relative errors of less than 13 and 5% in terms of the chord and twist profiles, respectively, and less than 0.3% in terms of efficiency, thrust, torque, and power coefficients. In addition, the design method computes optimal designs 170 times faster compared to the nonlinear programming algorithm. Based on these findings, we expect this new rotor design method to be useful in the conceptual design of rotary-wing aircraft.

关键词： Blade Element Momentum Theory Urban Air Mobility Rotary Wing Aircraft computing and informatics Aircraft Wing Design Aerodynamic Analysis Multidisciplinary Design and Optimization Aerodynamic Performance Momentum Theory Lagrange Multipliers

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Gradient-Based Optimization of Chaotic Panel Flutter

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AIAA JOURNAL 2023年第5期61卷 2234-2240页

作者： Stanford, Bret K. NASA Langley Res Ctr Aeroelast Branch Hampton VA 23681 USA

This work considers the gradient-based optimization of an aeroelastic panel structure undergoing chaotic vibrations. A previously presented adaptive time-marching scheme is used to stabilize the design sensitivities that would normally grow unbounded in time, owing to intermittent unstable eigenvalues of the Jacobian. The design optimization entails forcing the root mean square (RMS) of the panel vibration to some specified value, in a way that minimizes the deviation of the structural thickness variables from their uniform baseline values. Depending on the location of the RMS constraint, the output physics may be chaotic, limit cycle oscillations, or statically buckled.

关键词： Mechanical and Structural Vibrations Dynamic Pressure Flow Conditions Mathematical Analysis Structural Displacement computing and informatics Structural Nonlinearities Finite Element Solution Backward Euler Method Structural Kinematics and Dynamics

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Radiation-Induced Noise Resilience of Neuromorphic Architectures

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JOURNAL OF AEROSPACE INFORMATION SYSTEMS 2024年第1期21卷 75-83页

作者： Roffe, Seth Akolkar, Himanshu George, Alan Benosman, Ryad Univ Pittsburgh Dept Elect & Comp Engn 4420 Bayard St Suite 560 Pittsburgh PA 15260 USA Univ Pittsburgh Dept Neurobiol Biomed Sci Tower 3 Fifth Ave 4420 Bayard St Sui Pittsburgh PA 15213 USA Univ Pittsburgh Dept Elect & Comp Engn 4420 Bayard St Suite 560 Pittsburgh PA 15260 USA Carnegie Mellon Univ Pittsburgh PA 15213 USA Univ Pittsburgh Biosci Tower 3 Fifth Ave Pittsburgh PA USA

Neuromorphic event-based networks use asynchronous time-dependent information to extract features from input data that can allow for edge-based distributed applications such as object recognition. The noise resilience properties of such networks, especially in the context of space applications, are yet to be explored. In this paper, we use the hierarchy of time surfaces (HOTS) algorithm, which is one of the neuromorphic algorithms, to understand the least and most resilient modules in a neuromorphic network. The HOTS algorithm relies on the computing of time surfaces that maps the temporal delays between neighboring pixels into normalized features that involve many computations that are also found in other neuromorphic networks such as exponential decays, distance computations, etcetera. We implemented HOTS on a Digilent PYNQ board with a Xilinx Zynq 7020 system on a chip, and we subjected the boards running the HOTS network inference to neutron radiation at the Los Alamos Neutron Science Center. Furthermore, we used simulation models from our previous similar experiments on the event-based sensor to create a neutron induced noise model to quantify the effect of this noise on the overall performance of the network. This experiment provides the preliminary measurements of the reliability of the HOTS algorithm and proposes methods to create a more reliable HOTS architecture in future spacecraft missions.

关键词： Image Sensor Space Exploration and Technology computing and informatics Spacecraft Mission Integrated Circuits Convolutional Neural Network Gaussian Mixture Models Neuromorphic Engineering Event-Based Computation Radiation Testing

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Leveraging Machine Learning for Generating and Utilizing Motion Primitives in Adversarial Environments

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JOURNAL OF AEROSPACE INFORMATION SYSTEMS 2024年第2期21卷 127-139页

作者： Goddard, Zachary C. Rajasekar, Rithesh Mocharla, Madhumita Manaster, Garrett Williams, Kyle Mazumdar, Anirban Georgia Inst Technol Woodruff Sch Mech Engn Atlanta GA 30332 USA Georgia Inst Technol Sch Comp Sci Atlanta GA 30332 USA Sandia Natl Labs Pathfinder Technol Albuquerque NM 87123 USA

Motion primitives enable fast planning for complex and dynamic environments. Adversarial environments pose a particularly challenging and unpredictable scenario. Motion-primitive-based planners have the potential to provide benefit in these types of environments. The key challenge is to design a library of maneuvers that effectively capture the necessary capabilities of the vehicle. This work presents a primitive-based game tree search to solve adversarial games in continuous state and action spaces and applies a reinforcement learning framework to autonomously generate effective primitives for the given task. The results demonstrate the ability of the learning framework to produce maneuvers necessary for competing against adversaries. Furthermore, we propose a method for learning a model to estimate the state-dependent value of each motion primitives and demonstrate how to incorporate this model to increase planning performance under time constraints. Additionally, we compare our primitive-based algorithm against forward simulated methods from existing literature and highlight the benefits of motion primitives.

关键词： Reinforcement Learning Search Algorithm Artificial Neural Network Optimization Algorithm Proportional Control computing and informatics Monte Carlo Method

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Autonomous Spacecraft Attitude Reorientation Using Robust Sampled-Data Control Barrier Functions

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JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2023年第10期46卷 1874-1891页

作者： Breeden, Joseph Panagou, Dimitra Univ Michigan Dept Aerosp Engn Ann Arbor MI 48104 USA Univ Michigan Dept Robot Ann Arbor MI 48104 USA

This paper presents a provably safe method for constrained reorientation of a spacecraft in the presence of input constraints, bounded disturbances, and fixed frequency zero-order-hold (ZOH) control inputs. The set of states satisfying all pointing and rate constraints, herein called the safe set, is expressed as the intersection of the sublevel sets of several constraint functions, which are subsequently converted into control barrier functions (CBFs). The method then extends prior results on utilizing CBFs with ZOH controllers to the case of relative-degree-2 constraint functions, as occurs in the constrained attitude reorientation problem. The developed sampled-data controller is also shown to remain provably safe in the presence of input constraints and bounded disturbances. Finally, the method is validated and compared to three prior approaches via both low-fidelity and mid-fidelity simulations.

关键词： Spacecraft Attitude Control Attitude Control Satellites computing and informatics Optimal Control Theory Nonlinear Control Theory Autonomous Guidance and Control Robot Trajectory Generation Spacecraft Autonomous Operations Convex Optimization

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Optimal Service Migration for Data and Reasoning Fabric

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JOURNAL OF AEROSPACE INFORMATION SYSTEMS 2023年第11期20卷 785-793页

作者： Stepanyan, Vahram Schuet, Stefan Krishnakumar, Kalmanje KBR Wyle Serv LLC Moffett Field CA 94035 USA NASA Ames Res Ctr Intelligent Syst Div Mail Stop 269-1 Moffett Field CA 94035 USA NASA Ames Res Ctr Mail Stop 269-1 Moffett Field CA 94035 USA

In this paper we consider migration problem for Data and Reasoning Fabric (DRF)-enabled airspace operations assuming a fixed cloud/edge infrastructure with allocated computing, storage, and power resources, where cloud/edge servers and communication stations are in a wired connected network, while vehicles use a wireless network for communication. The objective is to automatically select the best location for the requested service execution, which achieves minimum cost while satisfying the user quality of service (QoS) and available resources constraints. To this end, estimates of the response time, consumed energy, and total cost are defined for each potential compute location. A mixed-integer linear program is then formulated and solved to identify optimal compute locations given QoS constraints and network infrastructure limitations, with worst-case vehicle positioning. The approach is applied to trajectory replanning use case to avoid a collision with an emergency vehicle in real time.

关键词： computing and informatics Airspace Management Advanced Air Mobility Data and Services Exchange computing and Communication Network Optimal Time and Energy Consumption

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An Assessment of the Laminar Hypersonic Double-Cone Experiments in the LENS-XX Tunnel

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AIAA JOURNAL 2023年第8期61卷 3298-3312页

作者： Ray, Jaideep Blonigan, Patrick Phipps, Eric T. Maupin, Kathryn Sandia Natl Labs Extreme Scale Data Sci & Analyt MS 9152 Livermore CA 94550 USA Sandia Natl Labs Scalable Algorithms Albuquerque NM 87185 USA Sandia Natl Labs Optimizat & Uncertainty Quantificat MS 1318 Albuquerque NM 87185 USA

This is an investigation on two experimental datasets of laminar hypersonic flows, over a double-cone geometry, acquired in Calspan-University at Buffalo Research Center's Large Energy National Shock (LENS)-XX expansion tunnel. These datasets have yet to be modeled accurately. A previous paper suggested that this could partly be due to mis-specified inlet conditions. The authors of this paper solved a Bayesian inverse problem to infer the inlet conditions of the LENS-XX test section and found that in one case they lay outside the uncertainty bounds specified in the experimental dataset. However, the inference was performed using approximate surrogate models. In this paper, the experimental datasets are revisited and inversions for the tunnel test-section inlet conditions are performed with a Navier-Stokes simulator. The inversion is deterministic and can provide uncertainty bounds on the inlet conditions under a Gaussian assumption. It was found that deterministic inversion yields inlet conditions that do not agree with what was stated in the experiments. An a posteriori method is also presented to check the validity of the Gaussian assumption for the posterior distribution. This paper contributes to ongoing work on the assessment of datasets from challenging experiments conducted in extreme environments, where the experimental apparatus is pushed to the margins of its design and performance envelopes.

关键词： Inverse Problems Uncertainty Quantification Computational Fluid Dynamics computing and informatics Hypersonic Flows

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Hybrid Method of Uncertainty Propagation for Near-Earth Conjunction Analysis

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JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2024年第9期47卷 1778-1791页

作者： Khatri, Yashica Scheeres, Daniel J. Univ Colorado Boulder Aerosp Engn Dept Boulder CO 80303 USA

In this research, several existing semi-analytical dynamics and uncertainty propagation techniques are combined with conjunction models to achieve fast and accurate probability of collision results. Initial Gaussian uncertainty associated with two objects is split into smaller Gaussian distributions using Gaussian mixture models to achieve mixture components that will maintain linearity over longer propagation times. These mixture components are propagated forward using second-order state transition tensors that can capture the nonlinearity in the propagation accurately by taking into account the desired dynamics. The dynamic solution and these tensors are computed using the Deprit-Lie averaging approach, including transformations between mean and osculating states, which accounts for perturbations due to solar radiation pressure and J2. This simplified dynamic system allows fast and accurate propagation by combining the speed of propagation with averaged dynamics and the accuracy of short-period variation addition. Combined, these mathematical tools are used to propagate the object's uncertainties forward. The final distributions are compared using analytical conjunction methods to compute the probability of collision, which is then compared to the Monte Carlo result to confirm the method's validity.

关键词： Planets Gaussian Mixture Models Solar Radiation Pressure Monte Carlo Analysis Resident Space Object Satellites Optical Properties Computer Programming and Language Space Situational Awareness computing and informatics

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