scholarly journals A Comprehensive Review on the Related Models in Magneto-Rheological Automobile Suspension System

2020 ◽  
Vol 13 (7) ◽  
pp. 1700
Author(s):  
S. F. Yaakub ◽  
S. H. Yahaya ◽  
F. Ahmad ◽  
M.S. Salleh ◽  
A.R.M. Warikh
Keyword(s):  
Suspension System ◽  
Comprehensive Review ◽  
Automobile Suspension ◽  
Magneto Rheological

Research on the Decoupling Control Algorithm of Full Vehicle Semi-Active Suspension

2012 ◽  
Vol 479-481 ◽  
pp. 1355-1360
Author(s):  
Jian Guo Chen ◽  
Jun Sheng Cheng ◽  
Yong Hong Nie
Keyword(s):  
Differential Geometry ◽  
Control Algorithm ◽  
Active Suspension ◽  
Suspension System ◽  
Decoupling Control ◽  
Vehicle Suspension ◽  
Full Vehicle ◽  
Vibration Attenuation ◽  
Magneto Rheological ◽  
Suspension Model

Vehicle suspension is a MIMO coupling nonlinear system; its vibration couples that of the tires. When magneto-rheological dampers are adopted to attenuate vibration of the sprung mass, the damping forces of the dampers need to be distributed. For the suspension without decoupling, the vibration attenuation is difficult to be controlled precisely. In order to attenuate the vibration of the vehicle effectively, a nonlinear full vehicle semi-active suspension model is proposed. Considering the realization of the control of magneto-rheological dampers, a hysteretic polynomial damper model is adopted. A differential geometry approach is used to decouple the nonlinear suspension system, so that the wheels and sprung mass become independent linear subsystems and independent to each other. A control rule of vibration attenuation is designed, by which the control current applied to the magneto-rheological damper is calculated, and used for the decoupled suspension system. The simulations show that the acceleration of the sprung mass is attenuated greatly, which indicates that the control algorithm is effective and the hysteretic polynomial damper model is practicable.

Control Bifurcations in a Nonlinear Active Suspension System for System Design

2005 ◽  
Author(s):  
Amit Shukla ◽  
Jeong Hoi Koo
Keyword(s):  
Control System ◽  
Ride Comfort ◽  
Active Suspension ◽  
Nonlinear Damping ◽  
Suspension System ◽  
Suspension Systems ◽  
System A ◽  
Controlled Systems ◽  
Automotive Suspension ◽  
Magneto Rheological

Nonlinear active suspension systems are very popular in the automotive applications. They include nonlinear stiffness and nonlinear damping elements. One of the types of damping element is a magneto-rheological fluid based damper which is receiving increased attention in the applications to the automotive suspension systems. Latest trends in suspension systems also include electronically controlled systems which provide advanced system performance and integration with various processes to improve vehicle ride comfort, handling and stability. A control bifurcation of a nonlinear system typically occurs when its linear approximation loses stabilizability. These control bifurcations are different from the classical bifurcation where qualitative stability of the equilibrium point changes. Any nonlinear control system can also exhibit control bifurcations. In this paper, control bifurcations of the nonlinear active suspension system, modeled as a two degree of freedom system, are analyzed. It is shown that the system looses stability via Hopf bifurcation. Parametric control bifurcation analysis is conducted and results presented to highlight the significance of the design of control system for nonlinear active suspension system. A framework for the design of feedback using the parametric analysis for the control bifurcations is proposed and illustrated for the nonlinear active suspension system.

Intelligent fuzzy logic with firefly algorithm and particle swarm optimization for semi-active suspension system using magneto-rheological damper

2016 ◽  
Vol 23 (3) ◽  
pp. 501-514 ◽  
Author(s):  
Mat Hussin Ab Talib ◽  
Intan Zaurah Mat Darus
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Firefly Algorithm ◽  
Ride Comfort ◽  
Particle Swarm ◽  
Mr Damper ◽  
Suspension System ◽  
System Response ◽  
Swarm Optimization ◽  
Magneto Rheological

This paper presents a new approach for intelligent fuzzy logic (IFL) controller tuning via firefly algorithm (FA) and particle swarm optimization (PSO) for a semi-active (SA) suspension system using a magneto-rheological (MR) damper. The SA suspension system’s mathematical model is established based on quarter vehicles. The MR damper is used to change a conventional damper system to an intelligent damper. It contains a magnetic polarizable particle suspended in a liquid form. The Bouc–Wen model of a MR damper is used to determine the required damping force based on force–displacement and force–velocity characteristics. The performance of the IFL controller optimized by FA and PSO is investigated for control of a MR damper system. The gain scaling of the IFL controller is optimized using FA and PSO techniques in order to achieve the lowest mean square error (MSE) of the system response. The performance of the proposed controllers is then compared with an uncontrolled system in terms of body displacement, body acceleration, suspension deflection, and tire deflection. Two bump disturbance signals and sinusoidal signals are implemented into the system. The simulation results demonstrate that the PSO-tuned IFL exhibits an improvement in ride comfort and has the smallest MSE for acceleration analysis. In addition, the FA-tuned IFL has been proven better than IFL–PSO and uncontrolled systems for both road profile conditions in terms of displacement analysis.

Adaptive control model of an active automobile suspension system

1993 ◽  
Author(s):  
Matthew Fritz ◽  
Donald C. Wunsch II ◽  
Sunanda Mitra
Keyword(s):  
Adaptive Control ◽  
Control Model ◽  
Suspension System ◽  
Automobile Suspension

A GSA-based adaptive fuzzy PID-controller for an active suspension system

2016 ◽  
Vol 33 (6) ◽  
pp. 1659-1667 ◽  
Author(s):  
Chun-Tang Chao ◽  
Ming-Tang Liu ◽  
Juing-Shian Chiou ◽  
Yi-Jung Huang ◽  
Chi-Jo Wang
Keyword(s):  
Pid Controller ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Suspension System ◽  
Fuzzy Pid ◽  
Content Type ◽  
Fuzzy Pid Controller ◽  
Fast Learning ◽  
Automobile Suspension ◽  
Hybrid Fuzzy Pid

Purpose – The purpose of this paper is to propose a novel design for determining the optimal hybrid fuzzy PID-controller of an active automobile suspension system, employing the gravitational search algorithm (GSA). Design/methodology/approach – The hybrid fuzzy PID-controller structure is an improvement to fuzzy PID-controller by incorporating a fast learning PID-controller. Findings – The GSA can adjust the parameters of the PID-controller to achieve the optimal performance. Research limitations/implications – The GSA may have the advantage of quick convergence, but the required computation may be intensive. Practical implications – The simulation results demonstrate the effectiveness of the proposed approach on active automobile suspension system. Originality/value – In order to demonstrate the theoretical guarantee of the proposed method, comparisons with particle swarm optimization or other methods has also been carried out.

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