Aircraft Flight Control Allocation With Real-Time Actuator 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 system constraints, allowing for dynamic adjustment based on current conditions and sensor data.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If pre-computed solutions and heuristics are used to determine actuator combinations, then the system complexity is reduced and ease of operation is improved, but the adaptability to handle all possible conditions and circumstances deteriorates

Engineering Contradiction:

Improveease of determining actuator combinationsVSAvoidability to handle all possible conditions

Core Design Contradiction:

Ease of operationVSAdaptability or versatility

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 the solution to adapt dynamically to changing flight conditions, sensor data, and actuator states rather than relying on pre-stored lookup tables for all possible conditions.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system changes the operational parameters from fixed pre-computed values to variable real-time optimized values. By formulating and solving optimization problems with changing cost functions and constraints based on current flight conditions, the system determines actuator combinations that are optimal for each specific moment rather than using generic pre-computed solutions.

Inventive Principle:

Principle #35Parameter changes

2Adaptability or versatility

If online optimization is performed to determine optimal actuator combinations in real-time, then the adaptability to current conditions is improved, but the computational complexity and processing time increase

Engineering Contradiction:

Improveability to respond to current conditionsVSAvoidcomputational complexity of optimization

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system performs preliminary formulation of optimization problems with pre-defined cost functions and constraints based on flight conditions. By preparing the optimization framework in advance with appropriate objective functions (such as minimizing power consumption) and constraints (such as actuator saturation limits), the system enables rapid real-time solution without ad-hoc computational complexity during critical flight moments.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system replaces traditional mechanical control allocation methods with computational optimization approaches. Instead of using fixed mechanical control laws or heuristic rules, the system uses mathematical optimization algorithms to determine actuator combinations, substituting computational intelligence for mechanical control logic to achieve greater adaptability.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Use of energy by moving object

If online optimization is used to minimize power consumption, then the energy efficiency is improved, but the computational resources and processing power required increase

Engineering Contradiction:

Improvepower consumption of actuatorsVSAvoidprocessing power for optimization

Core Design Contradiction:

Use of energy by moving objectVSPower

Solution Approach 1:

The system uses feedback from sensors and actuator states to continuously update the optimization problem. By incorporating real-time feedback on flight conditions, actuator positions, and power consumption rates into the cost function, the system optimizes actuator combinations to minimize power usage while adapting to changing conditions, creating a closed-loop control system that balances energy efficiency with computational requirements.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS12071231B2Online optimization-based flight control system

Publication Date: 2024.08.27 WISK AERO LLC

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.