Aircraft Supervisory Control for Integrated Power and Thermal Loads
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Solution Overview
Problem
Next-generation military aircraft require an advanced control system that integrates engine, air cycle, and electrical systems for power and thermal management to handle complex missions with increased demands and loads, but existing systems lack efficient coordination and real-time optimization.
Innovation Solution
A supervisory controller system that receives mission objectives and constraints, processes data from subsystems, generates reference commands, and sends optimized control signals to local controllers, using Model Predictive Control and distributed architecture to enforce constraints and achieve system objectives, including integrated propulsion, power, and thermal management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a supervisory controller with real-time optimization is implemented to coordinate subsystems, then system coordination and mission objective achievement are improved, but device complexity increases
Solution Approach 1:
The control system is divided into multiple levels: supervisory controllers at the aircraft level and local subsystem controllers at the component level. This segmentation allows distributed decision-making where the supervisory controller handles high-level coordination and optimization, while local controllers manage immediate subsystem operations, reducing the computational burden on any single controller and improving overall system reliability.
Solution Approach 2:
The supervisory controller acts as an intermediary between the mission objectives and the local subsystem controllers. It receives mission-level commands and translates them into coordinated reference commands for multiple subsystems, enabling seamless integration without direct complex interactions between individual subsystems.
2Reliability
If Model Predictive Control with constrained optimization is used to enforce system constraints, then constraint enforcement and safety are improved, but computational load and processing time increase
Solution Approach 1:
The Model Predictive Control algorithm performs preliminary calculations by predicting future system states and optimizing control actions over a forecast horizon. By pre-computing optimal control sequences based on predicted trajectories, the system can enforce constraints proactively rather than reactively, reducing the need for intensive real-time computation during critical moments.
Solution Approach 2:
The control system dynamically adjusts the optimization horizon and computational effort based on flight conditions and constraint criticality. During normal operations, a longer prediction horizon may be used for optimal performance, while during critical phases or emergencies, the system adapts to use shorter horizons and faster computation to meet real-time requirements.
Data Source
AI summary
A system including a supervisory controller configured to receive one or more mission objectives for an aircraft mission, and condition data; a memory for storing program instructions; at least one local supervisory controller, operatively coupled to the memory, and in communication with the supervisory controller and operative to execute program instructions to: simulate execution of the aircraft mission to address at least one of the one or more mission objectives; receive data output from at least one subsystem, the data output including a measurement of an aircraft physical system; generate a mission plan executable to address at least one of the one or more mission objectives via manipulation of the at least one subsystem; receive the generated mission plan at a subsystem controller directly from the at least one local supervisory controller; and automatically execute the generated mission plan to operate an aircraft.