Model predictive control based on ADMM for aero-engine

被引：0

作者：

Shan R. ^{[1
]}

Li Q. ^{[1
]}

He F. ^{[1
]}

Feng H. ^{[1
]}

Guan T. ^{[1
]}

机构：

[1] Jiangsu Province Key Laboratory of Aerospace Power System, College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing

来源：

Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics | 2019年 / 45卷 / 06期

关键词：

Aero-engine; Alternating direction method of multipliers (ADMM); Model predictive control; Quadratic programming (QP); Real time;

D O I：

10.13700/j.bh.1001-5965.2018.0599

中图分类号：

学科分类号：

摘要：

In order to improve the real time performance of the nonlinear model predictive control (MPC) for aero-engine, an alternating direction method of multipliers (ADMM) was applied to the receding horizon optimization of MPC. The predictive equation was constructed based on the state space model. The auxiliary variables and dual variables were introduced to rewrite the quadratic control performance index and engine constraints into a new form which could be solved by ADMM. Simulations on a component level model show that the single input variable model predictive control based on ADMM achieves both high-quality reference tracking performance and efficient limit management of aero-engine. Compared with interior point method (IPM), the real time performance of ADMM is much better than that of IPM at different magnitude control commands, and the increment of time consumption is much less than that of IPM with the increase of the predictive horizon. The effectiveness of the ADMM in MPC is valid. © 2019, Editorial Board of JBUAA. All right reserved.

引用

页码：1240 / 1247

页数：7

共 22 条

[1] Brunell B.J., Bitmead R.R., Connolly A.J., Nonlinear model predictive control of an aircraft gas turbine engine, 41st IEEE Conference on Decision and Control, 4, pp. 4649-4651, (2003)
[2] Brunell B.J., Viassolo D.E., Prasanth R., Model adaptation and nonlinear model predictive control of an aircraft engine, ASME Turbo Expo 2004: Power for Land, Sea, and Air, pp. 673-682, (2004)
[3] Brunell B.J., Mathews H.K., Kumar A., Adaptive model-based control systems and methods for controlling a gas turbine
[4] Richter H., Singaraju A.V., Litt J.S., Multiplexed predictive control of a large commercial turbofan engine, Journal of Guidance, Control, and Dynamics, 31, 2, pp. 273-281, (2008)
[5] Vroemen B.G., Van Essen H.A., Van Steenhoven A.A., Et al., Nonlinear model predictive control of a laboratory gas turbine installation, Journal of Engineering for Gas Turbines and Power, 121, 4, pp. 629-634, (1999)
[6] Du X., Application of sliding mode control and model predictive control to limit management for aero-engines, (2016)
[7] Lau M.S.K., Yue S.P., Ling K.V., Et al., A comparison of interior point and active set methods for FPGA implementation of model predictive control, European Control Conference, pp. 156-161, (2009)
[8] Shahzad A., Kerrigan E.C., Constantinides G.A., A warm-start interior-point method for predictive control, UKACC International Conference on Control, pp. 949-954, (2010)
[9] Eckstein J., Splitting methods for monotone operators with applications to parallel optimization, (1989)
[10] Boyd S., Parikh N., Chu E., Et al., Distributed optimization and statistical learning via the alternating direction method of multipliers, Foundations and Trends in Machine Learning, 3, 1, pp. 1-122, (2011)

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