Hypersonic Glide Trajectory Steering With Fast AoA Optimization
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Solution Overview
Problem
Conventional trajectory determination techniques for hypersonic vehicles are limited by simplifying assumptions and require expensive, powerful computers with long execution times, making them impractical for real-time steering and Monte Carlo analyses.
Innovation Solution
A fast algorithm for computing optimal trajectories using physical constants, initial angle of attack, and rate, which increments flight parameters to adjust the vehicle's trajectory dynamically, incorporating realistic models of the Earth's gravity and atmosphere for precise steering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional trajectory determination techniques are used, then trajectory optimization can be achieved, but computational cost is high and execution time is long
Solution Approach 1:
The trajectory optimization problem is divided into two distinct phases: an ascent phase and a descent phase. Each phase has its own set of boundary conditions and optimization objectives. The ascent phase optimizes from launch to peak altitude, while the descent phase optimizes from peak altitude to landing. This segmentation allows for more efficient computation by treating each phase separately with phase-specific constraints and objectives.
Solution Approach 2:
The patent performs preliminary computation of optimal trajectories offline using detailed atmospheric models and vehicle performance data. These pre-computed trajectories are stored in lookup tables that can be quickly accessed during real-time operations. The preliminary action includes computing trajectories for various initial conditions, atmospheric densities, and mission parameters, creating a database of optimal solutions that can be rapidly retrieved without requiring complex real-time calculations.
2Measurement precision
If conventional trajectory determination techniques are used, then trajectory optimization can be achieved, but computational resources required are excessive
Solution Approach 1:
The patent replaces expensive, complex trajectory optimization computations with simple, lightweight lookup table queries. Instead of requiring powerful computers to solve complex differential equations in real-time, the system uses pre-computed trajectory data stored in accessible memory. This substitution of heavy computational objects with lightweight data retrieval operations dramatically reduces the computational resource requirements while maintaining trajectory optimization accuracy.
Solution Approach 2:
The patent creates simplified copies of the optimal trajectory solutions in the form of lookup tables. These tables contain pre-computed trajectory parameters (angle of attack, flight path angle, velocity) for various mission conditions. During real-time operation, the system copies the relevant trajectory parameters from the lookup table based on current atmospheric density and mission parameters, avoiding the need to perform complex optimization calculations on-board the vehicle.
3Productivity
If simplifying assumptions are made in trajectory computation, then computation speed increases, but accuracy of results decreases
Solution Approach 1:
The patent performs preliminary computations using accurate, complex atmospheric models and vehicle performance data to generate lookup tables. During real-time operation, the system simply retrieves pre-computed values from these tables rather than performing complex calculations. This preliminary action transfers the computational burden from real-time operation to offline preparation, allowing fast real-time performance without sacrificing accuracy.
Solution Approach 2:
The patent creates simplified representations (copies) of complex trajectory solutions in lookup tables. These tables contain the essential trajectory parameters computed using accurate models, but stored in a format that allows rapid retrieval. The copying process captures the results of complex computations in a simplified data structure that can be quickly accessed without requiring the original computational complexity.
Data Source
AI summary
A computer implemented method is provided for in-flight trajectory steering a vehicle by an optimal path to a destination. This includes incorporating physical constants; setting initial angle of attack (AoA) and initial AoA rate; incrementing flight AoA; measuring operation parameters; establishing a flight trajectory; calculating an optimal trajectory; comparing flight trajectories; and commanding flight control. The physical constants include gravity and atmospheric conditions. The flight AoA increments from the initial AoA and any prior increments. The operation parameters of the vehicle include pressure, velocity and flight path angle. The flight trajectory denotes the vehicle's path to its destination based on the operation parameters using the physical constants. The optimal trajectory is based on with altitude and velocity of the vehicle. The flight trajectory is compared to the optimal trajectory as a steering correction by altering the flight AoA. The vehicle's flight control executes the steering correction at the flight AoA.