Aircraft Flight Control Actuator Mixing Under Real-Time Optimization
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Solution Overview
Problem
Existing flight control systems struggle to determine the optimal combination of actuators and associated parameters in real-time to achieve desired aircraft movements efficiently and effectively under varying conditions, as they rely on pre-computed heuristics that fail to account for all possible scenarios.
Innovation Solution
An online optimization system that dynamically determines the optimal mix of actuators and parameters using a bounded-variable least-squares approach, considering constraints and costs, to minimize error and power consumption, while accounting for real-time sensor data and actuator health.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If pre-computed heuristics are used to determine actuator combinations, then device complexity is reduced, but adaptability to varying flight conditions deteriorates
Solution Approach 1:
The system transitions from static pre-computed heuristics to dynamic online optimization that continuously adapts actuator combinations based on real-time flight conditions, sensor data, and actuator health status, resolving the contradiction between system complexity and adaptability
Solution Approach 2:
The optimization algorithm dynamically adjusts actuator parameters (position, speed, force) and selection based on changing flight conditions, enabling the system to adapt to varying scenarios without requiring complex reconfiguration of the overall control architecture
2Adaptability or versatility
If online optimization is implemented to dynamically determine optimal actuator combinations, then adaptability improves, but device complexity increases
Solution Approach 1:
The online optimization framework serves multiple functions simultaneously: it determines actuator combinations, optimizes parameters, handles failures, and adapts to conditions, consolidating these capabilities into a single universal control mechanism that manages complexity rather than increasing it
3Productivity
If more actuators are used to achieve desired movements, then productivity improves, but use of energy increases
Solution Approach 1:
The optimization algorithm determines the minimal sufficient set of actuators needed to achieve the desired flight control objective, avoiding unnecessary activation of all available actuators and thus reducing energy consumption while maintaining productivity
Solution Approach 2:
The system dynamically adjusts actuator parameters to optimize the balance between achieving desired movements and minimizing energy consumption, finding the optimal operating point for each actuator based on real-time conditions
4Reliability
If actuator combinations are optimized in real-time, then reliability under failures improves, but computing time increases
Solution Approach 1:
The system performs preliminary assessments of actuator health and availability, and pre-evaluates potential actuator combinations, enabling rapid reconfiguration when failures occur without requiring extensive real-time computation during critical moments
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.