Health-Adaptive Thruster Control for Spacecraft RCS
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Solution Overview
Problem
Current spacecraft Reaction Control Systems (RCS) face challenges in adapting to uncertainties and thruster failures, are limited by complex switching curves, and struggle with cross-axis coupling and computational intensity, leading to inefficient propellant consumption and limited thruster utilization.
Innovation Solution
A thruster control system that estimates vehicle responses, compares them to desired states, and develops commands to correct errors, while selecting optimal thruster combinations and maintaining health parameters using prognostic and diagnostic data to adapt to changes and failures, enabling autonomous operation and extended mission life.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If single axis switching curve logic is used for thruster control, then the control system is simple to implement, but cross-axis coupling produces disturbances and the system cannot adapt to uncertainties
Solution Approach 1:
The patent implements a dynamic thruster control system that continuously adapts to changing vehicle conditions and uncertainties. The control algorithm dynamically adjusts thruster selection and firing commands based on real-time state estimates and health parameters, transforming the static switching curve approach into a dynamic adaptive system that can handle cross-axis coupling and unexpected changes in mass properties or thruster dispersions.
Solution Approach 2:
The system incorporates feedback mechanisms by continuously monitoring vehicle state, comparing actual performance against desired responses, and adjusting thruster commands accordingly. The control algorithm uses feedback from health parameters and state estimates to adaptively select optimal thruster combinations, enabling the system to compensate for uncertainties and maintain reliable operation.
2Speed
If pre-calculated thruster selection tables are used, then the control response is fast, but the number of usable thruster combinations is limited
Solution Approach 1:
The patent replaces static pre-calculated tables with a dynamic thruster selection algorithm that can generate optimal thruster combinations in real-time. The system dynamically determines which thrusters to use based on current vehicle state, desired response, and health parameters, allowing full utilization of available thrusters rather than being constrained to predetermined combinations.
Solution Approach 2:
The control system performs self-service by autonomously selecting optimal thruster combinations without requiring pre-computed tables. The algorithm independently evaluates available thrusters based on current conditions and health status, making real-time decisions about which thrusters to activate and at what levels, thereby achieving both fast response and full adaptability.
3Loss of energy
If linear programming based thruster selection algorithms are used, then propellant consumption is minimized, but the algorithms are computationally intensive and require iterative convergence
Solution Approach 1:
The patent extracts the essential optimization objective (propellant minimization) from complex iterative linear programming algorithms. The control system implements a simplified selection algorithm that directly identifies optimal thruster combinations without requiring iterative convergence, achieving propellant efficiency through direct calculation rather than repeated optimization cycles.
Solution Approach 2:
The system replaces computationally expensive iterative algorithms with a simpler, non-iterative selection method. This disposable approach sacrifices the rigorous optimality guarantees of linear programming in exchange for dramatically reduced computational load and elimination of convergence issues, while still achieving effective propellant management.
4Adaptability or versatility
If the control system uses all available thrusters, then thruster utilization is optimized, but the computational load increases
Solution Approach 1:
The patent implements a dynamic thruster utilization strategy that adapts the number and selection of active thrusters based on current vehicle state and health parameters. The system dynamically determines the optimal subset of thrusters to use, utilizing all available thrusters when conditions permit but reducing active thruster count when computational resources are constrained or health parameters indicate limitations.
Data Source
AI summary
Methods and apparatuses are provided for managing a state of a platform. The methods and apparatuses comprise receiving a desired state for the platform. The methods and apparatuses comprise receiving the state of the platform. The methods and apparatuses comprise selecting a combination of thrusters from a plurality of thrusters to operate. The combination of thrusters is selected to adjust the state of the platform to the desired state. The methods and apparatuses operate the combination of thrusters to adjust the state of the platform to the desired state.