Quarter Car Passive Suspension Research Articles

Optimization and Analysis of the Quarter Car Passive Suspension Using Taguchi, Genetic Algorithm, and Simulated Annealing Approaches

Optimization and Analysis of the Quarter Car Passive Suspension Using Taguchi, Genetic Algorithm, and Simulated Annealing Approaches

State Observer Designs for Quarter-car Passive Suspension

State Observer Designs for Quarter-car Passive Suspension

State Observer Designs for Quarter-car Passive Suspension

State Observer Designs for Quarter-car Passive Suspension

Experimental and analytical investigations of friction at lubricant bearings in passive suspension systems

Friction is a very complex phenomenon, arising from the contact of surfaces. In many engineering applications, the success of models in predicting experimental results remains strongly sensitive to the friction model. In practice, it is not possible to determine an exact friction model; however, based on observation results and dynamic systems analysis, a recently proposed model of nonlinear friction at linear supported lubricant bearings is investigated. This model involved static friction, stiction region, and dynamic friction, which is consisted of transition, Stribeck effect, Coulomb and viscous frictions. On the other hand, this model is applied in the passive suspension system. Accordingly, a new quarter-car passive suspension model with the implementation of friction force is considered. Also, a vital experimental and simulation aspect is the generation of system input. Therefore, a nonlinear hydraulic actuator used, modelling this actuator including the dynamic of servovalve derived by the proportional-integral (PI) controller, is prepared. This study is validated experimentally, with simulation achieving C++ compiler. Consequently, a good agreement between the experimental and simulation results is obtained, i.e., the nonlinear friction, passive suspension system and nonlinear hydraulic actuator models are entirely accurate and useful. The suggested PI controller successfully derived the hydraulic actuator to validate the control scheme.

Validation of Simulation and Analytical Model of Nonlinear Passive Vehicle Suspension System for Quarter Car

In this paper the nonlinear behavior of passive suspension system for quarter car is studied. The mathematical model is developed with the help of equations of motions. In the actual practice, all system behaves nonlinearly but for simplification purpose it consider as linear. The quarter car two degree of freedom and MATLAB-SIMULINK model is developed for the nonlinear quarter car passive suspension system. The SIMULINK model is also validated with analytical model by State-Space approach. This paper is also emphasis the effect of nonlinear behavior of spring stiffness measured experimentally with the linear assumption of stiffness using validated SIMULINK model

Speed Effect to a Quarter Car ARX Model Based on System Identification

This paper presents the effect of car speeds on a quarter car passive suspension system model dynamics. The model is identified using system identification technique, in which the input-output data are collected by running a test car on an artificial road surface with two different speeds i.e., 10 km/h and 20 km/h. The quarter car passive suspension system dynamics is assumed to have an ARX model structure and identified using linear least-square estimation algorithm. The car vertical body acceleration, which is the output variable, is measured by installing an accelerometer sensor on the car body, above the suspension. On the other hand, the car shaft acceleration, which is the input variable, is measured by installing an accelerometer sensor at the lower arm of the car suspension. The best model for the 10 km/h car speed gives the output order () = 4, the input order () = 2, delay (<em>d)</em> = 1, the best fit = 90.65%, and the Akaike’s Final Prediction Error (FPE) = 5.315e-06. In contrast, the 20 km/h speed results in 4^th output order (), 1^stthe input order (), 1^st delay (<em>d)</em>, the best fit of 91.05%, and 7.503e-05Akaike’s FPE. These results show that the higher speed reduces the effect of the road surface to car dynamics, which is indicated by the order of the model

AN INTRODUCTION TO SISTEM IDENTIFICATION FOR A QUARTER CAR PASSIVE SUSPENSION

At present, as a method of establishing mathematical model of the system, the system identification has been widely applied to the automatic control, aviation, spaceflight, astronomy, medicine, biology, marine ecology and society, economics and many other fields. With the rapid development of science and technology, the status of system identification technique in various disciplines is becoming increasingly important. This paper introduces an system identification used for a quarter car passive suspension and a review previous research.

Multi-objective optimization of quarter car passive suspension design in the frequency domain based on PSO

Purpose – The purpose of this paper is to investigate the optimum design of a quarter car passive suspension system using a particle swarm optimization algorithm in order to minimize the applied loads and vibrations. Design/methodology/approach – The road excitation is assumed as zero-mean random field and modeled by single-sided power spectral density (PSD) based on international standard ISO 8608. The variance of sprung mass displacements and variance of dynamic applied load are evaluated by PSD functions and used as cost function for the optimization. Findings – The advantages in using this methodology are emphasized by an example of the multi-objective optimization design of suspension parameters and the results are compared with values reported in the literature and other gradient based and heuristic algorithms. The paper shows that the algorithm effectively leads to reliable results for suspension parameters with low computational effort. Research limitations/implications – The procedure is applied to a quarter car passive suspension design. Practical implications – The proposed procedure implies substantial time savings due to frequency domain analysis. Social implications – The paper proposes a procedure that allows complex optimization designs to be feasible and cost effective. Originality/value – The design optimization is performed in the frequency domain taking into account standard defined road profiles PSD without the need to simulate in the time domain.

Neuro Model Approach for a Quarter Car Passive Suspension Systems

The road handling, load carrying and passenger comfort are three intension factors on car suspension’s system. They should be compromised to achieve the good the car suspension dynamics. To fulfill the requirement, the car suspension system must be controlled and analyzed. To design and analyze the suspension controller, the realistic dynamics model of car suspension is needed. In this paper, the car suspension is assumed as a quarter car and has a model structure as a neural network structure. The model is assumed consist of nonlinear properties that are contributed by spring stiffness and damping elements of suspension system. The tire is assumed has linear properties and represented by spring stiffness element and damping element. The model responses are generated in simulation term. The random type of artificial road surface signal as an input variable is used in this simulation. The results show that the trend of neuro model have the same with the response of a quarter car nonlinear model from dynamic derivation. It means that the developed neuro model structure capable to represent the nonlinear model of a quarter car passive suspension system dynamics.

Experimental analysis of 2 DOF quarter-car passive and hydraulic active suspension systems for ride comfort

This paper describes an experimental analysis of 2 degree-of-freedom (DOF) quarter-car passive suspension system and hydraulic active suspension system (QC-H-ASS) for ride comfort. The passive suspension system, which models a quarter-car suspension, consists of the sprung mass, unsprung mass, a suspension spring and damper and a tyre spring. A hydraulic actuator has been considered as one of the most viable choices for an active suspension system due to its high power-to-weight ratio and low cost. Thus this model is modified to a 2 DOF QC-H-ASS by placing a hydraulic actuator, with its attendant control instrumentation, in between sprung and unsprung masses. The results show considerable improvement in ride comfort over the conventional passive system. The details of the quarter-car model development with the test set-ups for the passive and hydraulic active suspension systems, suspension elements employed, experimental analysis and results are presented.

System Identification of Hammerstein Model a Quarter Car Passive Suspension Systems Using Multilayer Perceptron Neural Networks (MPNN)

Sejak kebelakangan ini, ramai penyelidik telah memberi perhatian kepada aplikasi rangkaian neural untuk pengenalpastian sistem. Dalam penulisan ini, model Hammerstein untuk suspensi pasif kereta suku telah dikenal pasti menggunakan rangkaian perceptron neural anggapan berbilang lapis. Data masukan dan keluaran telah diperolehi dengan memandu sebuah kereta pada satu paras jalan yang khas. Struktur daripada rangkaian adalah berdasarkan kepada model daripada sistem. Rangkaian algoritma pembelajaran adalah berdasarkan kaedah pemarkahan Fisher. Maklumat Fisher ini diberikan sebagai pemberat matrix kovarian masukan dan keluaran lapisan rangkaian yang tersembunyi. Unitwise daripada kaedah pemarkahan Fisher berkurangan kepada algoritma di mana setiap unit menganggarkan sendiri pemberat dengan menggunakan kaedah pemberat kuasa dua terkecil. Hasilnya telah menunjukkan bahawa nilai ralat minimum punca knasa dua proses pembelajaran diperoleh dengan satu lelaran yang singkat. Kata kunci: Pengenalpastian sistem, model Hammerstein, rangkaian perceptron neural berbilang lapis, pemberat kuasa dua terkecil, maklumat Fisher, pemarkahan Fisher Recently, some researchers have focused on the applications of neural networks for system identification. In this paper, a Hammerstein model of a quarter car passive suspension system is identified using multilayer perceptron neural networks. Input and output data are acquired by driving a car on a special road event. The networks structure is based on system model. The network learning algorithm is based on Fisher’s scoring method. Fisher information is given as a weighted covariance matrix of inputs and outputs of the network hidden layer. Unitwise, Fisher’s scoring method reduces to the algorithm in which each unit estimates its own weights by a weighted least square method. The results show that the minimum mean square error (MSE) value of the training process was found with a short record. Key words: System identification, Hammerstein model, multilayer perceptron, weighted least square, Fisher information, Fisher’s scoring

Neural Network Based System Identification of an Axis of Car Suspension System

Neural networks system identification have been widely used for estimate the nonlinear model of system. In this paper, multilayer perceptron neural network is used for identifying the Nonlinear AutoRegressive with eXogenous input (NARX) model of a quarter car passive suspension system. Input output data are acquired by driving a car on a special road event. The networks structure is developed based on system model. The Networks learning algorithm is derived using Fisher’s scoring method. Then the Fisher information is given as a weighted covariance matrix of inputs and outputs the network hidden layer. Unitwise Fisher’s scoring method reduces to the algorithm in which each unit estimate its own weights by a weighted least square method. The results show that the method uses suitable for modeling a quarter car passive suspension systems.