AI Rocket Flight Control for Uncertain Weather Trajectories
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Solution Overview
Problem
Current rocket control systems are unable to generalize about environmental uncertainties, particularly in inclement weather conditions, leading to launch missions being limited and delayed, and there is a need for a control system that can adapt to stochastic dynamics for optimal performance.
Innovation Solution
An embedded device with a radiation-hardened processor and artificial intelligence computer program, utilizing deep reinforcement learning and convolutional neural networks, processes real-time sensor data to optimize rocket trajectory and landing performance in uncertain environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional control systems are used for rocket trajectory management, then the system structure is simple and reliable, but the system cannot adapt to environmental uncertainties and stochastic dynamics in inclement weather conditions
Solution Approach 1:
The patent replaces traditional mechanical control systems with an artificial intelligence-based software system embedded in a radiation-hardened processor. The AI computer program processes sensor data and generates control commands, substituting complex mechanical control mechanisms with intelligent software that can adapt to environmental uncertainties through learning and generalization capabilities.
Solution Approach 2:
The patent changes the operational parameters of the control system by implementing an AI program that can dynamically adjust control strategies based on real-time sensor data and environmental conditions. The system generalizes about the trajectory environment and adapts its behavior to stochastic dynamics, transforming a static control system into a dynamic, adaptive one.
2Reliability
If AI-based control systems are implemented to handle environmental uncertainties, then adaptability improves, but device complexity and radiation hardening requirements increase
Solution Approach 1:
The patent applies preliminary action by pre-training the AI computer program through simulation before deployment. The simulation training prepares the AI system to handle various environmental uncertainties and stochastic dynamics in advance, enabling it to perform reliably in uncertain environments without requiring complex real-time decision-making hardware.
Solution Approach 2:
The patent introduces a radiation-hardened processor as an intermediary between the AI software and the harsh space environment. This specialized hardware component protects the complex AI system from radiation damage while enabling the software to function reliably in uncertain environments, bridging the gap between sophisticated AI algorithms and harsh operational conditions.
3Manufacturing precision
If real-time sensor data processing is implemented for trajectory optimization, then landing precision improves, but computational requirements and processing time increase
Solution Approach 1:
The patent applies preliminary action by pre-training the AI system through extensive simulation before actual deployment. This pre-training enables the AI to quickly process real-time sensor data during actual flight without requiring extensive computational time, as the decision-making patterns have already been learned and optimized during simulation phase.
Solution Approach 2:
The patent implements feedback by continuously processing real-time sensor data and using it to adjust control commands for trajectory optimization. The AI system receives feedback from sensors about the current state and environmental conditions, processes this information, and generates corrected control commands to maintain precise trajectory and ensure accurate landing.
Data Source
AI summary
Rocket control is a difficult and unpredictable task in environments with inclement weather. As a result, launch missions are often strictly limited based on weather conditions. The present invention provides a device for controlling a rocket to account for environmental uncertainties and maintain optimal mission performance. In embodiments, the device is a radiation hardened field programmable gate array with an embedded artificial intelligence program contained in a graphics processing unit that is used for rocket reaction control by generating thrust vector commands.