PID Parameters Prediction Using Neural Network for A Linear Quarter Car Suspension Control

Abstract

Providing control for suspension systems in vehicles is an enhancing factor for comfort and safety. With the improvement of control conditions, it is possible to design a cost-efficient controller which will maintain optimum comfort within harsher environmental conditions. The aim of this study is to design an adaptive PID controller with a predictive neural network model, which will be referred as NPID (NeuralPID), to control a suspension system. For this purpose, a NN (Neural Network) model is designed to produce outputs for PID’s Proportional (P) parameter to provide optimum responses for different road inputs. Also, reliability of the system outputs, which is using adaptive Proportional parameter, is tested. PID parameters for linear quarter vehicle model are decided through Zeigler-Nichols method. An ideal PID model, where Integral (I) and Derivative (D) parameters are bound to Proportional parameter, is used in the system. When the outputs of different controlled and not controlled systems, which are free, PID and NPID, are compared; it has been seen that NPID outputs are more convenient. In addition, it is possible to design controllers, with adaptively adjusting P parameter, which are operating cost-effective.

Keywords

• Neural Network,PID,Quarter Car Model,2-DOF,Suspension Control,MATLAB

References

1. Fairley, T.E., 1995. Predicting the discomfort caused by tractor vibration. J. Ergon. 38 (10), 2091–2106
2. Deprez Koen, Moshou Dimitrios, Anthonis Jan, Improvement of vibrational comfort on agricultural vehicles by passive and semi-active cabin suspensions, Computers and Electronics in Agriculture 49 (2005) 431–440, Elsevier, 2005
3. Evers Willem-Jan, Besselink Igo, Teerhuis and Nijmeijer, On the achievable performance using variable geometry active secondary suspension systems in commercial vehicles, Vehicle System Dynamics, Vol. 49, No. 10, October 2011, 1553–1573
4. Ekoru John E. D, Prdro Jimoh O, Proportional-integral-derivative control of nonlinear half-car electro-hydraulic suspension systems, Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering) 2013 14(6):401-416
5. Mat Hussin Ab. Talib and Intan Z. Mat Darus, Self-Tuning PID Controller for Active Suspension System with Hydraulic Actuator, IEEE Symposium on Computers & Informatics, 2013
6. Prashant Mhaskar, Nael H. El-Farra and Panagiotis D. Christofides, A Method for PID Controller Tuning Using Nonlinear Control Techniques, Proceeding of the 2004 American Control Conference Boston, Massachusetts June 30 - July 2, 2004
7. Onat Cem, Yuksek Ismail, Sivrioglu Selim, Bir Aktif Süspansiyon Sistemi İçin H Kontrol Temeline Dayalı Doğrusal Olmayan Kontrolcü Tasarımı, Mühendis ve Makina Cilt: 47 Sayı: 554
8. Rao M.V.C. and Prahlad V, A tunable fuzzy logic controller for vehicle-active suspension systems, Fuzzy Sets and Systems 85 (1997) 11 21.

9. Ranjbar-Sahraie Bijan, Soltani Muhammad and Roopaie Mahdi, Control of Active Suspension System: An Interval Type -2 Fuzzy Approach, World Applied Sciences Journal 12 (12): 2218-2228, 2011.
10. Sakman Emir, Guclu Rahmi and Yagiz Nurkan, Fuzzy logic control of vehicle suspensions with dry friction nonlinearity, Sadhana Vol. 30, Part 5, October 2005, pp. 649–659.
11. Pekgokgoz R. K, Gurel M. A, Bilgehan M, Kisa M, Active Suspension Of Cars Usingfuzzy Logic Controller Optimized By Genetic Algorithm, International Journal of Engineering and Applied Sciences (IJEAS) Vol.2, Issue 4(2010)27-37
12. Jing Xu1, Juntao Fei1,2, Neural Network Predictive Control of Vehicle Suspension, College of Computer and Information1, Jiangsu Key Laboratory of Power Transmission and Distribution Equipment Technology2, Hohai University, Changzhou, 213022, P. R. China
13. Gulez Kayhan, Guclu Rahmi, CBA-Neural Network Control Of A Non-Linear Full Vehicle Model, Simulation Modelling Practice and Theory 16 (2008) 1163–1176
14. Dawei Liu, Huanming Chen, Rongchao Jiang, Wei Liu, Study of Ride Comfort of Active Suspension Based on Model Reference Neural Network Control System, Sixth International Conference on Natural Computation (ICNC 2010)
15. Ming-Chung Fang, Young-ZoungZhuo, Zi-YiLee, The Application Of The Self-Tuning Neural Network PID Controller On The Ship Roll Reduction İn Random Waves, Ocean Engineering 37 (2010) 529–538
16. Eski Ikbal, Yildirim Sahin, Vibration Control Of Vehicle Active Suspension System Using A New Robust Neural Network Control System, Simulation Modelling Practice and Theory 17 (2009) 778–793
17. Jimoh O. Pedro, Olurotimi A. Dahunsi, Nyiko Baloyi, Direct Adaptive Neural Control of a Quarter-Car Active Suspension System, IEEE Africon 2011 - The Falls Resort and Conference Centre, Livingstone, Zambia, 13 - 15 September
18. O. A. Dahunsi, J. O. Pedro, O. T. Nyandoro, System Identıfıcatıon And Neural Network Based PID Control Of Servo - Hydraulıc Vehıcle Suspensıon System, South Afrıcan Instıtute Of Electrıcal Engıneers, Vol.101(3) September 2010
19. Line Jeen Lian Ruey-Jing, Intelligent Control of Active Suspension Systems, IEEE Transaction On Industrial Electronics, Vol. 58, No. 2, February 2011