Passenger seat vibration control of a semi-active quarter car system with hybrid Fuzzy–PID approach

In this paper, semi-active quarter car system with three degrees of freedom is considered for modeling and evaluation of passenger ride comfort. Experimental results of magneto-rheological shock absorber are modeled using polynomial model. The considered algorithms in semi-active quarter car suspension system include PID controller, fuzzy logic controller, hybrid fuzzy–PID controller and hybrid fuzzy–PID controller with coupled rules. Simulation responses of the controlled semi-active and uncontrolled quarter car systems are compared under bump type of road excitation in time domain. Simulation results demonstrate that the semi-active suspension system having hybrid fuzzy–PID controller with coupled rules provide best performance in controlling the passenger seat acceleration and displacement response compared to uncontrolled and other controlled cases.

[1]  Luis Alvarez-Icaza,et al.  LuGre friction model for a magnetorheological damper , 2005 .

[2]  Shirley J. Dyke,et al.  PHENOMENOLOGICAL MODEL FOR MAGNETORHEOLOGICAL DAMPERS , 1997 .

[3]  Saban Cetin,et al.  Modeling and control of a nonlinear half-vehicle suspension system: a hybrid fuzzy logic approach , 2012 .

[4]  María Jesús López Boada,et al.  Modeling of a magnetorheological damper by recursive lazy learning , 2011 .

[5]  M. L. Aggarwal,et al.  COMPARATIVE ANALYSIS OF PASSENGER RIDE COMFORT USING VARIOUS SEMI- ACTIVE SUSPENSION ALTERNATIVES , 2014 .

[6]  Steve C. Southward,et al.  An Adaptive Semiactive Control Algorithm for Magnetorheological Suspension Systems , 2005 .

[7]  N. Wereley,et al.  Idealized Hysteresis Modeling of Electrorheological and Magnetorheological Dampers , 1998 .

[8]  Shouhu Xuan,et al.  Inverse neuro-fuzzy MR damper model and its application in vibration control of vehicle suspension system , 2012 .

[9]  T. T. Soong,et al.  A STOCHASTIC OPTIMAL SEMI-ACTIVE CONTROL STRATEGY FOR ER/MR DAMPERS , 2003 .

[10]  Rahmi Guclu,et al.  Semiactive Self-Tuning Fuzzy Logic Control of Full Vehicle Model with MR Damper , 2014 .

[11]  Mahmoud El-Kafafy,et al.  Automotive Ride Comfort Control Using MR Fluid Damper , 2012 .

[12]  Seung-Ik Lee,et al.  A hysteresis model for the field-dependent damping force of a magnetorheological damper , 2001 .

[13]  D. Gamota,et al.  Dynamic mechanical studies of electrorheological materials: Moderate frequencies , 1991 .

[14]  Miao Yu,et al.  Comparative research on semi-active control strategies for magneto-rheological suspension , 2010 .

[15]  Ali Volkan Akkaya,et al.  Simulation and hybrid fuzzy-PID control for positioning of a hydraulic system , 2010 .

[16]  Kum-Gil Sung,et al.  Design and Control of a MR Shock Absorber for Electronic Control Suspension , 2011 .

[17]  Nader Vahdati,et al.  Hybrid sliding mode control of semi-active suspension systems , 2009 .

[18]  Dean Karnopp,et al.  Vibration Control Using Semi-Active Force Generators , 1974 .

[19]  Mehdi Ahmadian,et al.  An evaluation of magneto rheological dampers for controlling gun recoil dynamics , 2001 .

[20]  Rahmi Guclu Fuzzy Logic Control of Seat Vibrations of a Non-Linear Full Vehicle Model , 2005 .

[21]  Seung-Bok Choi,et al.  Human simulated intelligent control of vehicle suspension system with MR dampers , 2009 .

[22]  James Lam,et al.  Modelling of a magneto-rheological damper by evolving radial basis function networks , 2006, Eng. Appl. Artif. Intell..

[23]  M A Rahman,et al.  Analysis and Experimental Study of Magnetorheological-Based Damper for Semiactive Suspension System Using Fuzzy Hybrids , 2011, IEEE Transactions on Industry Applications.

[24]  James Lam,et al.  Semi-active H∞ control of vehicle suspension with magneto-rheological dampers , 2005 .

[25]  Neil D. Sims,et al.  Hardware-in-the-loop simulation of magnetorheological dampers for vehicle suspension systems , 2007 .

[26]  Seung-Bok Choi,et al.  Optimal design of MR shock absorber and application to vehicle suspension , 2009 .

[27]  Shirley J. Dyke,et al.  Phenomenological Model of a Magnetorheological Damper , 1996 .

[28]  D. Hrovat,et al.  Survey of Advanced Suspension Developments and Related Optimal Control Applications, , 1997, Autom..

[29]  Gigih Priyandoko,et al.  PSO-optimised adaptive neuro-fuzzy system for magneto-rheological damper modelling , 2013 .

[30]  Mohd Azlan Hussain,et al.  Development of A Semi-Active Car Suspension Control System Using Magneto-Rheological Damper Model , 2007 .

[31]  Boris Lohmann,et al.  Application of LQ-based semi-active suspension control in a vehicle , 2011 .

[32]  Seonghun Park,et al.  Design and Performance Evaluation of a Rotary Magnetorheological Damper for Unmanned Vehicle Suspension Systems , 2013, TheScientificWorldJournal.

[33]  Wei-Hsin Liao,et al.  Modeling and control of magnetorheological fluid dampers using neural networks , 2005 .

[34]  Kuan-Yu Chen,et al.  A self-tuning fuzzy PID-type controller design for unbalance compensation in an active magnetic bearing , 2009, Expert Syst. Appl..

[35]  Gursel Alici,et al.  An Adaptive Neuro Fuzzy Hybrid Control Strategy for a Semiactive Suspension with Magneto Rheological Damper , 2014 .

[36]  Claudia Mara Dias Wilson,et al.  Structural vibration reduction using self-tuning fuzzy control of magnetorheological dampers , 2010 .