Flight Control Actuator Allocation Using Online Optimization
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Solution Overview
Problem
Existing flight control systems face challenges in determining the optimal combination of actuators and associated parameters to achieve desired forces and moments in real-time, as they often rely on pre-computed solutions and heuristics, which are not comprehensive enough to handle all possible conditions and circumstances.
Innovation Solution
A flight control system that performs online optimization by modeling costs and constraints to determine an optimal mix of actuators and parameters in real-time, using techniques such as bounded-variable least-squares to minimize a cost function and enforce linear constraints, allowing for dynamic adjustment based on current aircraft state and conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If pre-computed solutions and heuristics are used to determine actuator combinations, then device complexity is reduced, but adaptability to all possible conditions deteriorates
Solution Approach 1:
The system transitions from static pre-computed solutions to dynamic real-time optimization. The flight control system continuously solves optimization problems online to determine actuator combinations, allowing adaptation to changing flight conditions and unexpected scenarios that were not present in the pre-computed lookup tables.
Solution Approach 2:
The system changes the operational parameters from fixed pre-computed values to dynamically optimized values. By formulating and solving optimization problems with varying cost functions and constraints based on current flight state, the system adapts to all possible conditions while maintaining manageable complexity through structured mathematical approaches.
2Adaptability or versatility
If online optimization is performed to determine optimal actuator mix, then adaptability to all conditions improves, but computational complexity increases
Solution Approach 1:
The optimization problem is segmented into manageable components: cost function formulation, constraint definition, and solution computation. The system divides the complex actuator allocation problem into separate optimization objectives (power consumption, smooth transitions, saturation management) that can be addressed through structured mathematical programming.
Solution Approach 2:
The system replaces complex mechanical decision-making and heuristic algorithms with mathematical optimization techniques. By using bounded-variable least-squares and quadratic programming methods, the system achieves real-time optimization without requiring complex computational infrastructure, substituting mathematical elegance for computational complexity.
3Reliability
If more actuators are used to achieve desired forces and moments, then performance and reliability improve, but power consumption increases
Solution Approach 1:
The system dynamically changes actuator parameters (command values, deflection angles, thrust levels) to optimize power consumption while achieving desired forces and moments. The cost function explicitly penalizes high power consumption, and the optimization process finds parameter combinations that minimize energy use while satisfying performance requirements.
Solution Approach 2:
The system dynamically adjusts actuator usage based on real-time conditions. Rather than using fixed actuator combinations, the optimization process continuously determines the optimal mix of actuators and their parameters, allowing the system to achieve reliable performance while adapting power consumption to current flight conditions and actuator availability.
Data Source
AI summary
Techniques to control flight of an aircraft are disclosed. In various embodiments, a set of inputs associated with a requested set of forces and moments to be applied to the aircraft is received. An optimal mix of actuators and associated actuator parameters to achieve to an extent practical the requested forces and moments is determined.