Hybrid Control System for Reconfigurable Rotary Wing Aircraft
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Solution Overview
Problem
Controlling a rotary wing aircraft's dynamic response while adhering to numerous actuator and physical constraints is complex due to cross-coupling, requiring advanced multivariable control systems that can decouple responses and handle faults in real-time.
Innovation Solution
A hybrid control system combining model predictive control (MPC) and dynamic inversion modules, which receives operator commands, objectives, and constraints to manage reconfigurable elements, ensuring stability and robustness by dynamically allocating control resources and adapting to changing conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a multivariable control system is used to manage cross-coupled effectors and meet multiple goals and limits simultaneously, then control precision and reliability are improved, but device complexity and computational demand increase
Solution Approach 1:
The control system is segmented into multiple independent modules: a model predictive control module that handles high-level decision making and constraint management, and a dynamic inversion module that executes low-level control commands. This segmentation allows each module to specialize in specific control tasks, improving overall reliability while managing complexity through modular architecture.
Solution Approach 2:
The model predictive control module acts as an intermediary between operator commands and the dynamic inversion module. It receives commands, objectives, and constraints, processes them through optimization algorithms, and generates feasible control inputs that satisfy all constraints before passing them to the dynamic inversion module for execution.
2Measurement precision
If model predictive control with quadratic programming is used to solve constraint satisfaction problems in real-time, then control precision under constraints is improved, but computational time and processing demand increase
Solution Approach 1:
The system performs preliminary action by formulating the control problem as a quadratic programming problem with pre-defined constraints and objectives. The model predictive control module continuously solves this optimization problem at each control interval, predicting future system behavior and computing optimal control inputs that satisfy all constraints before execution is needed.
Solution Approach 2:
The control system incorporates feedback by continuously monitoring system state through sensors and using this information to update the quadratic programming problem formulation. The model predictive control module uses current state measurements to adjust control commands in real-time, ensuring precision while adapting to changing conditions and constraint violations.
Data Source
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AI summary
A control system (100) for a rotary wing aircraft having a reconfigurable element. The control system includes a model predictive control module (110)receiving operator commands (102), objectives (112) and constraints (106); and a dynamic inversion module (116) receiving an output (114) of the model predictive control module (110), the dynamic inversion module (116) providing control commands to reconfigure the reconfigurable element (118) of the rotary wing aircraft.