Simulation Study of Active Quarter Car Model Using Matlab And Simulink Software

International Journal of Mechanical Engineering

© 2020 by SSRG - IJME Journal

Volume 7 Issue 5

Year of Publication : 2020

Authors : Rahul Agrawal, Dr.Dev Dutt Singh

10.14445/23488360/IJME-V7I5P101

How to Cite?

Rahul Agrawal, Dr.Dev Dutt Singh, "Simulation Study of Active Quarter Car Model Using Matlab And Simulink Software," SSRG International Journal of Mechanical Engineering, vol. 7,  no. 5, pp. 1-7, 2020. Crossref, https://doi.org/10.14445/23488360/IJME-V7I5P101

Abstract:

This paper is to Improvement the Passenger Ride comfort,Vehicle stability, safety, Road Holding in an active Quarter car model. The main objective is to obtain a stable, robust, and controlled PID system. It is necessary to use the PID controller to increase the stability and performance of the System.The controller selection and design aimed to achieve good passenger ride comfort and health, stability, and passenger body acceleration and displacement Response under Uneven road excitations. The performance of the designed controlleris evaluated using simulation work in the time and frequency domain. Simulation results show that the proposed PID control scheme can successfully achieve the desired ride comfort and passenger safety compared to passive and PID controlled cases in an active quarter car model.Ride comfort is anImportant key issue in the design and Manufacture of modern automobiles. This paper addresses the ride comfort analysis of the quarter car model active suspension system. The active suspension system is proposed based on the Proportional Integral Derivative (PID) control technique to enhance its ride comfort. The ride comfort analysis of the System has been determined by computer simulation using MATLAB/Simulink.

Keywords:

Active suspension, PID controller, Passenger body, Quarter car model, Ride comfort.

References:

[1] H. Metered., Application of nonparametric Magnetorheological damper model in vehicle semi-activesuspension system, SAE International Journal of Passenger Cars, Mechanical Systems. 5 (2012) 715-726.
[2] Devdutt and M. L. Aggarwal., Fuzzy control of passenger ride performance using MR shock absorbersuspension in the quarter car model, International Journal of Dynamics and Control. 3 (4) (2015) 463-469.
[3] Devdutt and M. L. Aggarwal., Passenger seat vibration control of a semi-active quarter car system withhybrid Fuzzy – PID approach, International Journal of Dynamics and Control. 5(2) (2017) 287-296.
[4] D. Fischer and R. Isermann., Mechatronic semi-active and active vehicle suspensions, Control Engineering Practice. 12 (2004) 1353-1367.
[5] D. Hrovat., Survey of advanced suspension developments and related optimal control applications,Automatica. 33(10) (1997) 1781-817.
[6] J. Cao, P. Li, and H. Liu., A fuzzy interval controller for vehicle active suspension systems, IEEE.
[7] M. Heidari and H. Homaei., Design a PID Controller for Suspension System by Back PropagationNeural Network, Hindawi Publishing Corporation Journal of Engineering. Article ID 421543 (2013) 9.
[8] Devdutt., Self-Tuning Fuzzy Control of Seat Vibrations of Active Quarter Car Model, World Academy of Science, Engineering and Technology, 11(5) (2017) 1053-1059.
[9] W. Sun, H. Gao and O. Kaynak., Finite frequency H∞ control for vehicle active suspension systems, IEEE Trans, Control System Technology. 19 (2011) 416–22.
[10] J. Łuczko and U. Ferdek., Continuous And Discrete Sliding Mode Control Of An Active CarSuspension System, Journal of Theoretical And Applied Mechanics. 54(1) (2016) 3-11.
[11] M. Nagarkar, G. Vikhe, K. Borole, and V. Nandedkar., Active control of quarter-car suspension systemusing linear quadratic regulator, International Journal of Automotive Mechanical Engineering. 3 (2011) 364-372.
[12] L. Chai, T. Sun and J. Feng., Design of the LQG controller for active Suspension system based onanalytic hierarchy process, Automotive Engineering. 32(8) (2010) 712-718.
[13] DeepaPandey and Swati Kapoor., Design and Simulation of Fuzzy Controlled Suspension, SSRG International Journal of Electronics and Communication Engineering. 1(5) (2014) 16-18.
[14] S. Park and S. Rahmdel., A new fuzzy sliding mode controller with auto-adjustable saturation boundary layers implemented on vehicle suspension, International Journal of Engineering- Transactions C:Aspects. 26(12) (2013) 1401-1410.
[15] Q. Yun, Y. Zhao, and H. Yang., A dynamic sliding-mode controller with fuzzy adaptive tuning for anactive suspension system, Proc. IMechE Part D: Journal of Automobile Engineering, 221 (2007) 417-428.
[16] I. Fialho and G. J. Balas., Road adaptive active suspension design using linear parameter-varying gainscheduling, IEEE Transactions on Control System Technology. 10(1) (2002) 43–54.
[17] R. Kalaivani, K. Sudhagar, and P. Lakshmi., Neural Network-based Vibration Control for VehicleActive Suspension System, Indian Journal of Science and Technology, DOI:10.17485/ijst/2016/v9i1/83806. 9(1) (2016).
[18] R. Kothandaraman and L. Ponnusamy., PSO tuned Adaptive Neuro-fuzzy Controller for VehicleSuspension Systems, Journal of Advances in Information Technology. 3(1) (2012) 57-63.
[19] H. M. Fard and F. Samadi, Active Suspension System Control Using Adaptive Neuro-Fuzzy (ANFIS)Controller, IJE TRANSACTIONS C: Aspects. 28(3) (2015) 396-401.
[20] J.A. Moreno and M. Osorio., A Lyapunov approach to a second-order sliding mode controllers and observers, 47th IEEE Conference on Decision and Control, Mexico. 2856-2861.
[21] J. Rivera, L. Garcia, C. Mora, J.J. Raygoza, and S. Ortega., Super-Twisting Sliding mode in motion control system, in Sliding Mode Control, In-Tech. 237-254.
[22] J.S. Thongam, P. Bouchard, H. Ezzaidi, and M. Ouhrouche., Wind speed Sensor less maximum power point tracking control of variable speed wind energy conversion systems, IEEE International Electric Machines and Drives Conference, Miami, USA. (2009) 2195–2200.
[23] M. Zhou, G. Bao, and Y. Gong., Maximum power point tracking strategy for direct-driven PMSG, in Proceedings of the Asia-Pacific Power and Energy Engineering Conference, Wuhan, China. (2011) 1–4.
[24] A. Merabet, V. Rajasekaran,C A. McMullin, H. Ibrahim, R. Beguenane, and J. S. Thongam., Nonlinear model predictive controller with state observer for speed Sensorless induction generator–wind turbine systems, Journal of Systems and Control Engineering. 227(2) (2012) 198–213.
[25] Wind turbine experiment, Quick-Start Guide, Quanser Inc. Markham, Canada. (2010).
[26] Introduction to QUARC 2.0., Quanser Inc., Markham, Canada. (2010) 482.