Abstract
KEYWORDS – Air Suspension, Air Spring, Genetic Algorithm, Modeling, Optimization
ABSTRACT –
Research and/or Engineering Questions/Objective
Air-suspension is the suspension which supports a car body using an air-spring and elasticity of the compressed air. With the air spring in the car, it can satisfy the needs of variable drivers improved ride comfort and driving performance. Especially, by changing the air spring constant, the suspension stiffness can be controlled according to the driving purposes.
Methodology
Purpose of this study is analysis of the dynamic characteristics of the air spring system in air suspension, consist of a mathematical model and make an optimal control for a commercial vehicle. In this study, modeling the air spring model and using MATLAB / Simulink and It was verified to work with Carsim. Confirmed the simulation results based on the actually measured data, and the lacking data were applied to Genetic Algorithm theory to estimate the unknown parameters.
Results
In the study, to obtain various graphs including effective area and spring volume versus spring height graphs used in the air suspension modeling, a genetic algorithm is used to estimate the unknown parameters. The graph needs data which are obtained by complex tests. However, with genetic algorithm, the graph cannot be obtained without experiments on spring.
Limitations of this study
In the study, genetic algorithm finds the closest value based on the experimental value. The value is similar to the true value. But it cannot be sure exactly true value and it is the most important limitation of this study.
What does the paper offer that is new in the field including in comparison to other work by the authors? Finding the optimal parameters is important. With genetic algorithm which is used in this paper, the parameters can be obtained efficiently without complicated tests.
Conclusions
In the paper, the parameter estimates through genetic algorithm and the optimal solutions necessary for air suspension models is obtained. Furthermore, the following studies can be developed to infer the skyhook control by using a Genetic algorithm.