Intelligent Model Predictive Control and Its Application to Aeroengines
Publication: Journal of Aerospace Engineering
Volume 37, Issue 4
Abstract
In this paper, a new model predictive control termed as intelligent model predictive control (IMPC) combined with an improved new competitive swarm optimizer (CSO) is designed. The analytical predictive model is not necessarily established a priori in the proposed IMPC algorithm, and the control plant can be used directly as the predictive model to reduce the complexity of the algorithm. In addition, two new techniques, dynamic initialization and back steps methods, are proposed and utilized to improve the traditional CSO to realize constraints management during the optimization process. An application to aeroengine transient-state control is studied to verify the effectiveness of the presented IMPC algorithm. It is shown that, benefitting from the IMPC algorithm, the control task is well completed and all the constraints are satisfied.
Practical Applications
This paper proposes a new optimization algorithm to solve some complex optimization problems with constraints. The designed new optimization algorithm is simple and easy to implement and it is more suitable for applications with black-box models or complex optimization objective functions compared with some existing optimization algorithms. This paper may provide some help to researchers who are interested in model predictive control or metaheuristic algorithms and to people whose work contains some complex optimization problems. This paper also provides a design method of aeroengine transient-state control plans. The simulation results showed that compared with some existing control methods or optimization algorithms, the aeroengine could accelerate to the desired steady-state point with shorter transient time and all constraints are satisfied when utilizing the proposed new optimization algorithm.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This work was supported by the National Natural Science Foundation of China (No. 62203064) and the National Science and Technology Major Project (No. J2019-V-0010-0105).
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Information & Authors
Information
Published In
Journal of Aerospace Engineering
Volume 37 • Issue 4 • July 2024
Copyright
© 2024 American Society of Civil Engineers.
History
Received: Dec 13, 2022
Accepted: Jan 8, 2024
Published online: Mar 27, 2024
Published in print: Jul 1, 2024
Discussion open until: Aug 27, 2024
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