Aircraft Flight Control With Online Actuator Mix Optimization
Find Innovative SolutionsGenerate Solutions
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 that cannot account for all possible conditions and circumstances, leading to inefficiencies and potential saturation issues.
Innovation Solution
An online optimization system that models costs and constraints in real-time to determine an optimal mix of actuators and parameters, 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 are used to determine actuator combinations, then device complexity is reduced, but adaptability to different 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 based on current flight conditions, aircraft state, and pilot inputs, enabling adaptation to changing circumstances while maintaining manageable complexity through efficient algorithms.
Solution Approach 2:
The system changes the parameter being optimized from fixed pre-computed values to dynamically adjusted actuator commands. By formulating the problem as an online optimization task that adjusts actuator parameters in real-time based on current conditions, the system achieves both adaptability and computational efficiency.
2Adaptability or versatility
If online optimization is implemented to improve adaptability, then computational requirements increase
Solution Approach 1:
The system implements partial optimization by focusing computational efforts on the most critical aspects of flight control. Rather than optimizing all possible parameters simultaneously, the system targets key actuator combinations and flight parameters, achieving sufficient adaptability with reduced computational burden through selective optimization of essential control variables.
3Manufacturing precision
If more actuators are used to achieve desired forces and moments, then control precision improves, but risk of actuator saturation increases
Solution Approach 1:
The optimization system incorporates feedback about actuator states and constraints into the real-time decision-making process. By continuously monitoring actuator positions, rates, and saturation margins, the system adjusts actuator commands to achieve precise control while preventing saturation, using the optimization framework to balance precision requirements against reliability constraints.
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, including by minimizing a weighted set of costs that includes costs associated with one or more errors each corresponding to a difference between a requested force or moment and a corresponding force or moment achieved by the computed solution.