Constraint optimization of nonlinear McPherson suspension system using genetic algorithm and ADAMS software

Abstract

In this article, optimization of the McPherson suspension mechanism of a real car named Arisan is considered. In this regard, a model based on a real-life suspension system is proposed with the least simplification. This model is built in the ADAMS/View software based on the actual size of the suspension mechanism of Arisan. Moreover, the user-written code of the genetic algorithm in C is added as a plug-in to the ADAMS/View software in a completely innovative way to optimize the suspension system. 16 parameters of the suspension system are selected as design variables to wholly handle its geometry. The value of all design variables is optimally found by GA to minimize the variation of the camber angle as an objective function. Comparison of the obtained optimum suspension by the proposed method with the actual suspension system of Arisan shows a 23.5% improvement in the camber variation angle. It is worth noting that the proposed method does not require a mathematical model of the suspension system that leads to some simplifications such as linearization and non-friction joints. The proposed method can be used for modeling and optimization of other nonlinear dynamical systems such as robotics and building structures.