통찰 - Aerospace Engineering - # Joint Trajectory Replanning for Mars Ascent Vehicle under Propulsion System Faults

Optimized Trajectory Replanning for Mars Ascent Vehicle under Propulsion System Faults using a Suboptimal Learning-based Approach

Q: How can the proposed SJTR method be extended to handle other types of propulsion system faults beyond thrust drop, such as engine failures or propellant leaks?

The SJTR method can be adapted to address various propulsion system faults by modifying the underlying dynamics and optimization framework to account for the specific characteristics of each fault type. For instance, in the case of engine failures, the method could incorporate a model that simulates the loss of thrust from one or more engines, adjusting the thrust vector and magnitude accordingly. This would involve redefining the thrust model to reflect the operational status of the engines, potentially using a binary or continuous variable to represent the operational state of each engine. For propellant leaks, the SJTR method could integrate a dynamic model that accounts for the changing mass of the Mars ascent vehicle (MAV) over time due to the leak. This would necessitate real-time updates to the mass flow rate and the remaining propellant, which could be incorporated into the optimization constraints. Additionally, the learning-based warm-start approach could be trained on datasets that include scenarios with engine failures or propellant leaks, allowing the deep neural network (DNN) to provide initial guesses that are tailored to these specific fault conditions. Furthermore, the optimization problem could be expanded to include additional terminal constraints that reflect the new operational limits imposed by these faults. By systematically incorporating these modifications, the SJTR method can maintain its effectiveness and efficiency in trajectory replanning under a broader range of propulsion system failures.

Q: What are the potential limitations or drawbacks of the learning-based warm-start approach, and how can they be addressed to further improve the reliability and robustness of the trajectory replanning process?

One potential limitation of the learning-based warm-start approach is its reliance on the quality and diversity of the training dataset. If the dataset does not adequately cover the range of possible fault scenarios, the DNN may produce inaccurate initial guesses, leading to suboptimal or infeasible solutions during the trajectory replanning process. To mitigate this risk, it is essential to ensure that the training dataset is comprehensive, incorporating a wide variety of fault conditions, including edge cases and rare scenarios. Another drawback is the potential for overfitting, where the DNN learns to perform well on the training data but fails to generalize to unseen scenarios. To address this, techniques such as cross-validation, regularization, and dropout can be employed during the training process to enhance the model's robustness. Additionally, continuous learning strategies could be implemented, allowing the DNN to update its parameters based on new data collected during actual missions, thereby improving its predictive capabilities over time. Lastly, the computational overhead associated with training and deploying the DNN may introduce delays in the trajectory replanning process. To counter this, optimizing the architecture of the neural network for faster inference times, or employing model compression techniques, can help reduce the computational burden while maintaining accuracy.

Q: Given the importance of ensuring mission success for Mars ascent vehicles, how can the insights from this work be applied to the design and development of more advanced fault-tolerant control and decision-making systems for future space exploration missions?

The insights gained from the SJTR method and its learning-based warm-start approach can significantly inform the design of advanced fault-tolerant control and decision-making systems for future space exploration missions. Firstly, the integration of real-time fault detection and diagnosis capabilities is crucial. By employing machine learning algorithms that analyze telemetry data, mission control systems can quickly identify and classify propulsion system faults, enabling timely and informed decision-making. Moreover, the concept of joint trajectory optimization, as demonstrated in the SJTR method, can be extended to other mission phases and vehicle types. This holistic approach ensures that trajectory planning is not only reactive to faults but also proactive in optimizing mission objectives under uncertainty. Implementing adaptive control strategies that adjust flight parameters in real-time based on the current state of the vehicle and its environment can enhance mission resilience. Additionally, the development of simulation environments that replicate the Martian atmosphere and potential fault scenarios can facilitate rigorous testing and validation of fault-tolerant systems. By using these simulations to train and refine decision-making algorithms, engineers can ensure that the systems are robust against a wide range of contingencies. Finally, fostering collaboration between control engineers, data scientists, and mission planners will be essential in creating integrated systems that leverage data-driven insights for enhanced decision-making. This interdisciplinary approach can lead to the development of more sophisticated algorithms that not only prioritize mission success but also optimize resource utilization and safety in the face of unforeseen challenges during space exploration missions.

핵심 개념

A suboptimal joint trajectory replanning (SJTR) method is proposed to efficiently solve the problem of optimizing the target orbit and flight trajectory for a Mars ascent vehicle after encountering a thrust drop fault, by incorporating penalty coefficients for terminal constraints and utilizing a learning-based warm-start scheme.

초록

The content discusses the problem of trajectory replanning for a Mars ascent vehicle (MAV) when encountering a thrust drop fault in the propulsion system during launch.

The key highlights are:

A suboptimal joint trajectory replanning (SJTR) method is proposed, which formulates the joint optimization problem of target orbit and flight trajectory within a convex optimization framework. By incorporating penalty coefficients for terminal constraints, the SJTR method adheres to the orbit redecision principle, avoiding complex decision-making processes and providing a concise and rapid solution.
A learning-based warm-start scheme is designed in conjunction with the SJTR method. Offline, a deep neural network (DNN) is trained using a dataset generated by the SJTR method. Online, the DNN provides initial guesses for the time optimization variables based on the current fault situation, enhancing the solving efficiency and reliability of the algorithm.
Numerical simulations of the MAV flight scenario under thrust drop faults are performed, and Monte Carlo experiments and case studies across all orbit types demonstrate the effectiveness of the proposed method. Compared to the general trajectory replanning method, the SJTR with learning-based warm-start exhibits higher computational efficiency and solution feasibility, providing a non-optimal but highly reliable solution for hazardous Mars ascent missions.

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통계

The MAV has a total mass of 350 kg at takeoff, with a payload mass of 5 kg. The first stage has a dry mass of 27.6 kg, propellant mass of 196 kg, and specific impulse of 293 s. The second stage has a dry mass of 70.4 kg, propellant mass of 51 kg, and specific impulse of 315 s.

인용구

"During the Mars ascent vehicle (MAV) launch missions, when encountering a thrust drop type of propulsion system fault problem, the general trajectory replanning methods relying on step-by-step judgments may fail to make timely decisions, potentially leading to mission failure."
"To the best of the authors' knowledge, very few studies are currently available on MAV trajectory replanning after faults. This is a multi-phase, highly nonlinear, free-terminal time optimization problem that is inherently difficult to solve, requiring algorithms with high real-time capability."

핵심 통찰 요약

Joint Trajectory Replanning for Mars Ascent Vehicle under Propulsion System Faults: A Suboptimal Learning-Based Warm-Start Approach

by Kun Li, Guan... 게시일 arxiv.org 10-01-2024

https://arxiv.org/pdf/2409.19536.pdf

Joint Trajectory Replanning for Mars Ascent Vehicle under Propulsion System Faults: A Suboptimal Learning-Based Warm-Start Approach

더 깊은 질문

How can the proposed SJTR method be extended to handle other types of propulsion system faults beyond thrust drop, such as engine failures or propellant leaks?

The SJTR method can be adapted to address various propulsion system faults by modifying the underlying dynamics and optimization framework to account for the specific characteristics of each fault type. For instance, in the case of engine failures, the method could incorporate a model that simulates the loss of thrust from one or more engines, adjusting the thrust vector and magnitude accordingly. This would involve redefining the thrust model to reflect the operational status of the engines, potentially using a binary or continuous variable to represent the operational state of each engine.
For propellant leaks, the SJTR method could integrate a dynamic model that accounts for the changing mass of the Mars ascent vehicle (MAV) over time due to the leak. This would necessitate real-time updates to the mass flow rate and the remaining propellant, which could be incorporated into the optimization constraints. Additionally, the learning-based warm-start approach could be trained on datasets that include scenarios with engine failures or propellant leaks, allowing the deep neural network (DNN) to provide initial guesses that are tailored to these specific fault conditions.
Furthermore, the optimization problem could be expanded to include additional terminal constraints that reflect the new operational limits imposed by these faults. By systematically incorporating these modifications, the SJTR method can maintain its effectiveness and efficiency in trajectory replanning under a broader range of propulsion system failures.

What are the potential limitations or drawbacks of the learning-based warm-start approach, and how can they be addressed to further improve the reliability and robustness of the trajectory replanning process?

One potential limitation of the learning-based warm-start approach is its reliance on the quality and diversity of the training dataset. If the dataset does not adequately cover the range of possible fault scenarios, the DNN may produce inaccurate initial guesses, leading to suboptimal or infeasible solutions during the trajectory replanning process. To mitigate this risk, it is essential to ensure that the training dataset is comprehensive, incorporating a wide variety of fault conditions, including edge cases and rare scenarios.
Another drawback is the potential for overfitting, where the DNN learns to perform well on the training data but fails to generalize to unseen scenarios. To address this, techniques such as cross-validation, regularization, and dropout can be employed during the training process to enhance the model's robustness. Additionally, continuous learning strategies could be implemented, allowing the DNN to update its parameters based on new data collected during actual missions, thereby improving its predictive capabilities over time.
Lastly, the computational overhead associated with training and deploying the DNN may introduce delays in the trajectory replanning process. To counter this, optimizing the architecture of the neural network for faster inference times, or employing model compression techniques, can help reduce the computational burden while maintaining accuracy.

Given the importance of ensuring mission success for Mars ascent vehicles, how can the insights from this work be applied to the design and development of more advanced fault-tolerant control and decision-making systems for future space exploration missions?

The insights gained from the SJTR method and its learning-based warm-start approach can significantly inform the design of advanced fault-tolerant control and decision-making systems for future space exploration missions. Firstly, the integration of real-time fault detection and diagnosis capabilities is crucial. By employing machine learning algorithms that analyze telemetry data, mission control systems can quickly identify and classify propulsion system faults, enabling timely and informed decision-making.
Moreover, the concept of joint trajectory optimization, as demonstrated in the SJTR method, can be extended to other mission phases and vehicle types. This holistic approach ensures that trajectory planning is not only reactive to faults but also proactive in optimizing mission objectives under uncertainty. Implementing adaptive control strategies that adjust flight parameters in real-time based on the current state of the vehicle and its environment can enhance mission resilience.
Additionally, the development of simulation environments that replicate the Martian atmosphere and potential fault scenarios can facilitate rigorous testing and validation of fault-tolerant systems. By using these simulations to train and refine decision-making algorithms, engineers can ensure that the systems are robust against a wide range of contingencies.
Finally, fostering collaboration between control engineers, data scientists, and mission planners will be essential in creating integrated systems that leverage data-driven insights for enhanced decision-making. This interdisciplinary approach can lead to the development of more sophisticated algorithms that not only prioritize mission success but also optimize resource utilization and safety in the face of unforeseen challenges during space exploration missions.