Turbo-Electric Distributed Propulsion Control for Stable Power Sharing
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Solution Overview
Problem
Current turbo-electric distributed propulsion (TeDP) systems in aircraft and spacecraft face challenges in designing efficient, stable, and safe operations without increasing overall weight, particularly in time-varying missions, due to the lack of dynamic models and control designs that ensure fault-tolerance and optimization across wide operating ranges.
Innovation Solution
The development of a hierarchical control system that utilizes rigorous physics-based dynamic models and Lagrangian/Hamiltonian passivity-based control logic, integrating nonlinear dynamic power-electronic controllers to manage set points and ensure stability and efficiency, with automated feed-forward and feedback control mechanisms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional component-level design and control is used, then individual component performance is optimized, but system-level stability and fault-tolerance cannot be ensured
Solution Approach 1:
The control system is divided into hierarchical layers: system-level controllers that manage overall stability and fault-tolerance, and component-level controllers that optimize individual element performance. This segmentation allows each layer to focus on specific control objectives without overwhelming complexity.
Solution Approach 2:
System-level controllers act as intermediaries between the power source and individual components, coordinating their operation to ensure system-wide stability and fault-tolerance while allowing component-level optimization to continue.
2Reliability
If equipment is oversized to ensure stable operation, then reliability improves, but aircraft weight increases
Solution Approach 1:
The control system dynamically adjusts equipment operation based on real-time system conditions and predicted future states. This allows equipment to be sized for normal operation while the control system ensures stability during transient and fault conditions, reducing the need for oversized equipment.
Solution Approach 2:
The system uses predictive modeling and feed-forward control to anticipate future system states and adjust equipment operation in advance. This prevents instability before it occurs, allowing for more efficient equipment sizing rather than requiring oversized equipment to handle all possible scenarios.
3Productivity
If complex adaptive control systems are implemented, then system efficiency and stability improve, but device complexity increases
Solution Approach 1:
The control system uses autonomous algorithms that automatically adjust system operation based on real-time conditions and predictive models. This self-service capability achieves high efficiency and adaptability without requiring complex manual control systems or excessive human intervention.
Solution Approach 2:
The system dynamically changes operating parameters based on real-time conditions and predictive analysis. This allows the system to optimize efficiency across varying operating conditions without requiring a fundamentally complex control architecture, as the same hardware adapts through parameter adjustment.
4Measurement precision
If rigorous physics-based dynamic models are used, then control accuracy and stability guarantee improve, but computational complexity increases
Solution Approach 1:
The physics-based dynamic model is segmented into modular component models that can be independently developed, validated, and computed. This modular approach maintains high accuracy through rigorous physics-based formulations while reducing computational complexity through efficient model structure and localized computation.
Data Source
AI summary
Disclosed herein are methods and systems for modeling and controlling the disparate components (e.g. generators, storage, propulsors, and power electronics) that comprise an aircraft turbo-electric distributed power (TeDP) system. The resulting control system is hierarchical and interactive. Layer one is the physical electric power system. Layer three is an optimization system that determines set points for system operation. Layer two, in between layer one and layer three, includes nonlinear, fast, dynamic power-electronic controllers that hold the operation of the power system to the desired set points. Communication between these layers ensures feasibility and stability of the controlled operation. Simulations demonstrate that the resulting control system ensures stability and maximum efficiency.