Application of BP Neural Network PID Controller in Landing Gear Based on MRF Damper

Shi Xing Zhu; Yue Han; Bo Wang

doi:10.4028/www.scientific.net/AMM.779.226

Paper Titles

Distributed Embedded Control Architecture of a Leader-Follower Mobile Robot Formation
p.201

Performance Comparison of Displacement and Speed Control in Electro-Hydrostatic Actuator
p.205

Research on Fuzzy Control of MR (Magneto-Rheological) Damper for Landing Gear Based on FuzzyTECH
p.212

Position and Speed Double Closed Loop Control Performance of Electro-Hydraulic Actuator System Research Based on AMESim
p.220

Application of BP Neural Network PID Controller in Landing Gear Based on MRF Damper
p.226

Research and Simulation of Fuzzy PID Controller
p.233

Simulation of LQR Controller on Magnetorheological Landing Gear
p.239

Design of a Pneumatic Flow Rate Control Microvalve Driven by a Stepper-Motor
p.244

Design and Simulation for Electro-Hydraulic Proportional Tension Control System
p.250

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 779Application of BP Neural Network PID Controller in...

Application of BP Neural Network PID Controller in Landing Gear Based on MRF Damper

Abstract:

For characteristics of nonlinearity and time-varying volatility of landing gear based on MR damper, a BP neural network PID controller with a momentum was designed on basis of established dynamic mathematical model. BP neural network would adjust three parameters of PID online in time. The controller was inputted the energy which was combined by the feedback of acceleration and displacement of the control system, which greatly reduced the computation time of controller and the control effect was more obvious. After compared with PID, the simulation and experiment have showed that BP neural network PID has a better effect. The arithmetic can be put into practice through experimental testing.

You might also be interested in these eBooks

17th Hydrodynamic Electromechanical Control Engineering

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volume 779)

Pages:

226-232

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.779.226

Citation:

Cite this paper

Online since:

July 2015

Authors:

Shi Xing Zhu, Yue Han*, Bo Wang

Keywords:

BP-PID Control, Energy Feedback, Magneto-Rheological Fluid, Semi-Active Controller

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] Jinhua Li, Shuisheng Chen, Xihui Liu. The research of the semi-active control of magnetorheo –logical damper[J]. Journal of railway engineering, 2007 (8): 88-92.

Google Scholar

[2] Z D Xu, Y Q Guo. Integrated intelligent control analysis on semi-active structures by using magneto-rheological dampers [J]. Science in China Series E: Technological Sciences, 2008, 51(12): 2280-2294.

DOI: 10.1007/s11431-008-0209-3

Google Scholar

[3] Wujuan Jia. Aircraft landing gear fuzzy control based on magneto-rheological damper[J]. Journal of Beijing university of aeronautics and astronautics, 2008, 33(11): 1264-1267.

Google Scholar

[4] Jiadi Hong, Kailin Zhao. The research of semi-active suspension neural network adaptive control[J]. Micro computer information, 2008 (1).

Google Scholar

[5] Spooner J T, Passino K M. Stable adaptive control using fuzzy systems and neural networks[J]. Fuzzy Systems, IEEE Transactions on, 1996, 4(3): 339-359.

DOI: 10.1109/91.531775

Google Scholar

[6] Penghui Wang, Jinyu Xu, Weiming Li. The research of uneven Power spectral characteristic on airport road[J]. The Chinese and foreign road, 2008, 28(3): 50-53.

Google Scholar

[7] Dingyu Xue. Computer aided design about control system—MATLAB and application[M]. 2 Beijing: Tsinghua university publishing company, (2006).

Google Scholar

[8] Yuhong Tang, Xun Chen. Ac digital servo system based on the DSP and the neural network PID control[J]. Electrical automation, 2008, 29(6): 22-23.

Google Scholar