基于自适应模糊PID的飞机客舱温度控制

doi:10.16182/j.issn1004731x.joss.201811041

系统仿真学报 ›› 2018, Vol. 30 ›› Issue (11): 4395-4402.doi: 10.16182/j.issn1004731x.joss.201811041

基于自适应模糊PID的飞机客舱温度控制

李宗帅¹, 张思博²

1.中国民航大学电子信息与自动化学院,天津 300300;
2.北京空间飞行器总体设计部,北京 100094

收稿日期:2018-05-30 修回日期:2018-07-01 发布日期:2019-01-04

Designing a Self-Adaptive Fuzzy PID Controller for Aircraft Cabin Temperature

Li Zongshuai¹, Zhang Sibo²

1.School of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300,China;
2.Beijing Institute of Spacecraft System Engineering, Beijing 100094, China

Received:2018-05-30 Revised:2018-07-01 Published:2019-01-04
About author:Li Zongshuai(1982-), man, master, lecturer, research interests: Fuzzy control and electric drive.
Supported by:
National Natural Science Foundation of China(U1433107), The Fundamental?Research Funds for the Central Universities (3122017009)

摘要/Abstract

摘要： 飞机客舱温度控制系统具有高度非线性、不确定的特点,传统的PID控制很难取得良好的控制效果,因此设计了自适应模糊PID控制器。采用机理建模与实验相结合的方法,确定了系统的数学模型。针对自适应模糊PID控制器参数范围变动较大,不易调节的问题,提出了一种能够很方便确定模糊PID控制器比例、积分以及微分三个参数合理范围的方法。基于专家系统设计模糊规则。利用MATLAB/SIMULINK建立了仿真模型,仿真结果表明提出的自适应模糊PID控制器在抗系统参数摄动以及不确定方面具有更好的鲁棒性。

关键词: 模糊PID控制器, 飞机地面空调, 飞机客舱, 非线性, 不确定参数

Abstract: The temperature control system is a highly nonlinear and uncertainty system, and using a conventional PID controller makes it difficult to achieve a good control effect. In this paper, a self-adaptive fuzzy PID controller is described. The model of controlled object is established by combining mechanism modeling with experiment. Aiming at the problem that the parameter range of the self-adaptive fuzzy PID controller varies greatly and is difficult to adjust, a new method is proposed which can easily provide a more reasonable fuzzy controller output variable range. The fuzzy rules based on experts’ experience and knowledge are adopted. The simulation model is established using MATLAB/SIMULINK. The simulation results show that the self-adaptive fuzzy PID controller has better robust performance against system parameter changes and uncertainties.

Key words: fuzzy PID controller, airplane ground air conditioner, aircraft cabin, nonlinear, uncertainty parameter

中图分类号:

TP181

李宗帅, 张思博. 基于自适应模糊PID的飞机客舱温度控制[J]. 系统仿真学报, 2018, 30(11): 4395-4402.

Li Zongshuai, Zhang Sibo. Designing a Self-Adaptive Fuzzy PID Controller for Aircraft Cabin Temperature[J]. Journal of System Simulation, 2018, 30(11): 4395-4402.

参考文献

[1] Wang H O, Tanaka K, Griffin M F.An approach to fuzzy control of nonlinear systems: stability and design issues[J].IEEE Transactions on Fuzzy Systems (S1063-6706) , 2002, 4(1): 14-23.
[2] Şaban Çetin, Akkaya A V.Simulation and hybrid fuzzy-PID control for positioning of a hydraulic system[J]. Nonlinear Dynamics (S0924-090X), 2010, 61(3): 465-476.
[3] Jia D, You B.Study on novel plasma arc cutting technology based on PIDNN-FUZZY controller[J]. International Journal of Innovative Computing Information & Control Ijicic (S1349-4198), 2011, 7(7): 4171-4182.
[4] Li Y, Tong S, Li T.Adaptive fuzzy backstepping control design for a class of pure-feedback switched nonlinear systems[J]. Nonlinear Analysis: Hybrid Systems (S1751-570X), 2015, 16(2): 72-80.
[5] Xu Q, Kan J, Chen S, et al.Fuzzy PID Based Trajectory Tracking Control of Mobile Robot and its Simulation in Simulink[J]. International Journal of Control & Automation (S2207-6387), 2014, 7(8): 233-244.
[6] Mamdani E H.Application of fuzzy algorithms for control of simple dynamic plant[J]. Proceedings of the Institution of Electrical Engineers (S0020-3270), 1974, 121(121): 1585-1588.
[7] Bachache N, Wen J.PSO and GA designed Pareto of Fuzzy Controller in AC Motor Drive[J]. International Journal of Control & Automation (S2207-6387), 2013, 6(5): 149-158.
[8] Mugisha J C, Munyazikwiye B, Karími H R.Design of temperature control system using conventional PID and Intelligent Fuzzy Logic controller[C]. International Conference on Fuzzy Theory and ITS Applications. IEEE, 2016: 50-55.
[9] Baghli F Z, Bakkali L E.Design and Simulation of Robot Manipulator Position Control System Based on Adaptive Fuzzy PID Controller[M]. Robotics and Mechatronics. Springer International Publishing, 2016: 243-250.
[10] Huang H C, Xu S D, Chiang C H.Optimal Fuzzy Controller Design Using an Evolutionary Strategy-Based Particle Swarm Optimization for Redundant Wheeled Robots[J]. International Journal of Fuzzy Systems (S1562-2479), 2015, 17(3): 390-398.
[11] Yi T U.Simulation of Large-scale Aircraft Cabin Temperature Control System[J]. Acta Aeronautica Et Astronautica Sinica (S1000-6893), 2011, 32(1): 49-57.
[12] Fang D, Na F.Application and simulation of fuzzy neural network PID controller in the Aircraft cabin temperature[J]. Sensors & TransducersS(S2306-8515), 2013, 153(6): 100-104.
[13] Tu Y, Lin G P.Dynamic Simulation of Aircraft Environmental Control System Based on Flowmaster[J]. Journal of AircraftS(S0021-8669 ), 2012, 48(6): 2031-2041.
[14] Xie X, Long Z.Fuzzy PID Temperature Control System Design Based on Single Chip Microcomputer[J]. International Journal of Online Engineering (S1861-2121), 2015, 11(8): 29.
[15] Chiang W Y.Establishment and application of fuzzy decision rules: an empirical case of the air passenger market in Taiwan[J]. International Journal of Tourism Research (S1522-1970), 2011, 13(5): 447-456.

基于自适应模糊PID的飞机客舱温度控制

Designing a Self-Adaptive Fuzzy PID Controller for Aircraft Cabin Temperature

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