Total views : 361
Smart Adaptive Suspension System using MR Fluid
The objective is to develop a smart adaptive suspension system that dynamically varies the damping coefficient to achieve better control and improve the ride quality of an automobile. Adaptive/semi-active systems can only change the viscous damping coefficient of the shock absorber, and do not add energy to the suspension system. Though limited in their intervention, semi-active suspensions are less expensive to design and consume far less power. In recent times, research in semi-active suspensions has continued to advance on their capabilities, narrowing the gap between semi-active and fully active suspension systems. The various ways of achieving semi-active suspension systems are through solenoid/valve actuated, MR fluid and ER fluid mechanisms. Among the ways mentioned above, we chose to realize our objective using MR fluid damper system due to its favourable properties which are discussed later. It consists of an ultrasonic sensor to detect uneven terrain that needs specific hardening/softening of the suspension. The input to the system also includes the speed sensor. Once the central controller calculates the required stiffness of the suspension, it actuates a magnetic circuit that produces a magnetic field around the damper. The damper is filled with Magneto-Rheological fluid (MR Fluid) containing small iron filings. The magnetic field manipulates the non-Newtonian effects of the MR fluid to change the stiffness of the suspension system. This concept can be implemented for vibration control applications from automobiles to railway vehicles and civil structures. This system can be used in modern anti earth quake building base construction.
Adaptive Suspension System, IR Sensor, Magneto-Rheological Fluid (MR Fluid), MR Fluid Damper System, Ultrasonic Sensor.
- Kasprzyk J, Krauze P. Automotive MR damper modeling for semi-active vibration control. 2014 IEEE/ASME International Conference on Advanced Intelligent Mechatronics; 2014 Jul 8–11. p. 500–5.
- Lozoya-Santos J, Morales-Menendez R, Ramirez-Mendoza R. MR-damper based control system. Systems, Man, and Cybernetics. 2009 Oct 14:5168–73.
- Felix-Herran L, Mehdi D, Soto R, de Rodríguez-Ortiz J, Ramírez-Mendoza R. Control of a semi-active suspension with a magnetorheological damper modeled via Takagi-Sugeno. 2010 18th Mediterranean Conference on Control & Automation (MED); 2010 Jun 23–25. p. 1265–70.
- Zapatero M, Pozo F, Karimi HR, Luo N. Semi-active control methodologies for suspension control with magnetorheological dampers. IEEE/ASME Transactions on Mechatronics. 2011 Feb 17:370–80.
- Truong DQ, Ahn KK. MR fluid damper and its application to force sensor less damping control system, smart actuation and sensing systems - recent advances and future challenges. Berselli G, Vertechy R, Vassura G, editor. InTech; 2012 Oct 17. DOI: 10.5772/51391.
- Minorowicz B, Stefanski F. Proposal of a new group of magnetorheological dampers; 2014. p. 263–67. DOI: 10.12915/pe.2014.07.59.
- Sassi S, Cherif K. An innovative magnetorheological damper for automotive suspension: from design to experimental characterization. 2005 Jul 28.
- El-Kafafy M, El-Demerdash SM, Rabih A-AM. Automotive ride comfort control using MR fluid damper. 2012. p. 179–87. DOI: 10.23.2012.
- dos Santos FLM, Serpa AL et.al. Semi-active suspension control with one measurement sensor using H∞ technique. 9th Brazilian Conference on Dynamics; 2010 Jun 7.
- Guo S, Li S, Yang S. Semi-active vehicle suspension systems with magnetorheological dampers. Vehicular Electronics and Safety; 2006 Dec 13–15:403–6.
- Sakai C, Ohmori H, Sano A. Modeling of MR damper with hysteresis for adaptive vibration control. 42nd IEEE Conference on Decision and Control; 2003. p. 3840–5.
- Kang HI, Kang HS. A study on performance of passenger vehicles with suspension systems. Indian Journal of Science and Technology. 2015 Jan; 8(S1). DOI: 10.17485/ijst/2015/v8iS1/57926.
- Kumar MPJ, Goplakrishnan K, Srinivasan V, Anbazhagan R, Aanana JS. PC modelling and simulation of car suspension system. Indian Journal of Science and Technology. 2013 May; 6(S5). DOI: 10.17485/ijst/2013/v6i5S/33365.
- Kalaivani R, Sudhagar K, Lakshmi P. Neural network based vibration control for vehicle active suspension system. Indian Journal of Science and Technology. 2016 Jan; 9(1). DOI: 10.17485/ijst/2016/v9i1/83806.
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.