Landing Guidance Strategy of Space Rider

Figueroa-González, Antonio; Cacciatore, Francesco; Haya-Ramos, Rodrigo

doi:10.2514/1.a34957

Cited by 15 publications

(3 citation statements)

References 22 publications

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“…Today, the communications field intends to improve the performance of 5G ecosystems and beyond through non-terrestrial networks (NTNs) based on satellite constellations [174]. Furthermore, there is a trend to develop scientific experiments and create space architectures that use reusable vehicles to access space and return to Earth, such as the Space Rider, which has been conceived to provide a space laboratory for payloads to operate in orbit for a variety of applications [175].…”

Section: Cubesats and Beyond: Advances And Future Initiativesmentioning

confidence: 99%

Considerations for Eco-LeanSat Satellite Manufacturing and Recycling

Buitrago-Leiva,

Camps,

Moncada Niño

2024

Sustainability

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This research aims to contribute to the development of the Eco-LeanSat concept by focusing on a sustainable approach to satellite manufacturing and the repurposing of remaining satellite capabilities after failure. Despite satellites no longer being suitable for their original purposes, these remaining capabilities can find new applications. The study begins by identifying relevant innovative eco-design applications. Subsequently, it examines sustainability within the satellite lifecycle supply chain, categorizing it into four methods: (1) active debris removal, (2) transport logistics, (3) mission extension, and (4) repair and construction. Aligned with emerging trends in space activities, the study also considers future developments to maximize satellites’ potential to provide new services. Additionally, the research includes a description of a potential lean manufacturing process that encompasses logistic chains to support the development of a more sustainable space economy. Finally, the study concludes with a technological survey tracing the evolution of the development of the SmallSat and CubeSat platforms that identifies relevant innovative designs for a sustainable space environment.

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Section: Cubesats and Beyond: Advances And Future Initiativesmentioning

confidence: 99%

Considerations for Eco-LeanSat Satellite Manufacturing and Recycling

Buitrago-Leiva,

Camps,

Moncada Niño

2024

Sustainability

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“…The baseline GNC implements a waypoint-based guidance strategy for the first parts of the flight under parafoil but uses a heuristic path planning algorithm for the final part [7]. Based on these waypoints, the approach and landing phase is therefore split into several legs imposing different guidance profiles as illustrated in Figure 1.…”

Section: Benchmark Definitionmentioning

confidence: 99%

Enhancing the Guidance, Navigation and Control of Autonomous Parafoils using Machine Learning Methods

Hewing,

Gramlich,

Verhoek

et al. 2023

Papers of ESA GNC-ICATT 2023

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Artificial Intelligence techniques have developed into a transformative force across many industries. Their industrial adaption in aerospace Guidance, Navigation and Control (GNC) systems, however, has been rather limited to date. The “Artificial Intelligence for Guidance, Navigation and Control” (AI4GNC) project led by SENER Aeroespacial and funded by the European Space Agency (ESA) investigates the potential of several machine learning methods to enhance the performance and robustness of an aerospace GNC design. As a specific use case, we consider the descent and landing phase of an autonomous parafoil-guided return vehicle, inspired by the ESA Space Rider whose GNC software is developed by SENER. On this benchmark scenario, we demonstrate how a combination of machine learning methods can be used to significantly improve the performance of a baseline GNC design and gain insight into the system behaviour and its sensitivities. We investigate several complementing technologies at different hierarchical levels in the GNC and its design process and demonstrate the gained advantages on a full-complexity functional simulator, representative of industrial practice. At the intersection of guidance and controls, the project employs data-driven system identification to capture closed-loop system behaviour to serve as a basis for higher-level planning and guidance algorithms. Such models are typically cumbersome to derive from first principles since flight software, including lower-level controllers, actuator saturations and similar effects are part of the loop. In particular, neural networks which have been trained with an efficient deep-learning-based system identification method are used to augment an idealized baseline model which assumes perfect lower-level control. This is shown to effectively reduce residual errors while extending the region of validity compared to alternative linear variants and thereby provide an accurate system description to higher-level planning algorithms. Within the guidance layer, a robust trajectory planning technique is developed based on onboard optimization which can take numerous sources of uncertainty into account, such as wind conditions or uncertain system dynamics. The planning method relies on a novel extension of differential dynamic programming using results from robust control to formulate a sequence of semidefinite programs to find feedback & feedforward policies that efficiently steer the system despite the adversarial action of uncertainties. Extensive evaluations on the functional simulator show a clear hierarchy of achieved performances: (nominal) optimization-based guidance outperforms the baseline solution, while the novel robust variant shows the strongest performance. We furthermore present a developed GNC auto-tuner tool that utilizes Bayesian optimization (BO) to efficiently tune high-level GNC parameters for complex natural language constraints or objectives formulated in terms of temporal logic expressions. The use of Bayesian optimization enables a data-efficient stochastic black-box optimization of several key GNC parameters using a small number of (simulation) experiments. We further demonstrate that it is straightforward to employ the techniques in an antagonistic fashion leading to an effective worst-case-analysis tool. Our results show how such temporal logic-constrained BO can be efficiently used to improve system performance, explore parameter interdependencies and provide valuable insights to support the tuning of complex GNC systems. Finally, all developments are presented in a unified perspective highlighting synergies and sketching a general framework in which AI and data-driven techniques can contribute to the GNC discipline. This particularly highlights the increasingly central position of simulations, not only as a verification and validation tool but rather as an integral part of the GNC design process itself. With this, we envision a viable path forward towards the integration of AI techniques towards industrial practice, and towards realizing its considerable potential for the field.

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“…The influence of missile initial conditions' uncertainties and IMU errors on the impact point dispersion using lateral thrusters for control were investigated in [16] and the influence of uncertainties in rocket parameters on the performance of a cold launch were analyzed in [17]. IMU errors and noise impact on the guidance were also investigated in [18,19]. The analysis of the robustness of the control algorithm with respect to uncertainty regarding the launch environment and rocket conditions was presented in [20,21].…”

Section: Introductionmentioning

confidence: 99%

Study of Model Uncertainties Influence on the Impact Point Dispersion for a Gasodynamicaly Controlled Projectile

Jacewicz

Lichota

Miedziński

et al. 2022

Sensors

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The article presents the analysis of the impact point dispersion reduction using lateral correction thrusters. Two types of control algorithms are used and four sources of uncertainties are taken into account: aerodynamic parameters, thrust curve, initial conditions and IMU errors. The Monte Carlo approach was used for simulations and Circular Error Probable was used as a measure of dispersion. Generic rocket mathematical and simulation model was created in MATLAB/Simulink 2020b environment. Results show that the use of control algorithms greatly reduces the impact point dispersion.

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Landing Guidance Strategy of Space Rider

Cited by 15 publications

References 22 publications

Considerations for Eco-LeanSat Satellite Manufacturing and Recycling

Considerations for Eco-LeanSat Satellite Manufacturing and Recycling

Enhancing the Guidance, Navigation and Control of Autonomous Parafoils using Machine Learning Methods

Study of Model Uncertainties Influence on the Impact Point Dispersion for a Gasodynamicaly Controlled Projectile

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