Presented By: Aerospace Engineering
Dissertation Defense: Advanced Predictive Control Strategies for More Electric Aircraft
William Dunham
William Dunham
Dissertation Committee:
Professor Ilya Kolmanovsky (co-chair)
Associate Professor Anouck Girard (co-chair)
Dr Brandon Hencey, Air Force Research Laboratory
Professor Jing Sun, Naval Architecture and Marine Engineering (Cognate)
Presentation Info:
February 21st, 2019
GM Conference Room, Lurie Engineering Center
Next generation aircraft designs are incorporating more extensive electrical distributions that cover a broader range of applications, increasing the power levels to be met and the complexity of their operation. The expansion of the electrical grid cascades out into the engine, where the generators extract power from. This dissertation develops advanced predictive control strategies that account for the interactions between the subsystems in order to enable the potential benefits of a More Electric Aircraft (MEA), such as improved efficiency and reliability.
First, models representing the engine and power subsystems of the MEA, including their interactions, are developed. The control objective in this MEA system is to actuate the engine and power subsystem inputs to satisfy demands for thrust and power loads while enforcing constraints on compressor surge
and bus voltage deviations.
Second, model predictive control (MPC) strategies incorporating disturbance rejection, coordination between the subsystems, and anticipation of the changes in the power loads are shown to be effective in the MEA.
Third, a Distributed MPC is formulated that accounts for separately developed subsystems through controller privacy and differences in update rates.
Finally, a Scenario Based MPC is proposed to handle stochastic transitions in the thrust and power load references.
Dissertation Committee:
Professor Ilya Kolmanovsky (co-chair)
Associate Professor Anouck Girard (co-chair)
Dr Brandon Hencey, Air Force Research Laboratory
Professor Jing Sun, Naval Architecture and Marine Engineering (Cognate)
Presentation Info:
February 21st, 2019
GM Conference Room, Lurie Engineering Center
Next generation aircraft designs are incorporating more extensive electrical distributions that cover a broader range of applications, increasing the power levels to be met and the complexity of their operation. The expansion of the electrical grid cascades out into the engine, where the generators extract power from. This dissertation develops advanced predictive control strategies that account for the interactions between the subsystems in order to enable the potential benefits of a More Electric Aircraft (MEA), such as improved efficiency and reliability.
First, models representing the engine and power subsystems of the MEA, including their interactions, are developed. The control objective in this MEA system is to actuate the engine and power subsystem inputs to satisfy demands for thrust and power loads while enforcing constraints on compressor surge
and bus voltage deviations.
Second, model predictive control (MPC) strategies incorporating disturbance rejection, coordination between the subsystems, and anticipation of the changes in the power loads are shown to be effective in the MEA.
Third, a Distributed MPC is formulated that accounts for separately developed subsystems through controller privacy and differences in update rates.
Finally, a Scenario Based MPC is proposed to handle stochastic transitions in the thrust and power load references.
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