Multi-objective optimization of vehicle seat suspension with friction under random excitation

Abstract

A seat suspension contributes greatly to vehicle ride comfort as a result of direct contact with the human body. Friction in a seat suspension produces strong non-smooth nonlinearity in seat dynamics, which makes the simulation-based optimization on the seat suspension’s performance time-consuming. This study tries to address parameter optimization on a vehicle seat suspension with the friction force in an analytical approach. A two degrees of freedom model is firstly established for the human body-seat system with friction and subjected to bandlimited random excitation. The nonlinear model is converted into an equivalent linear model by using Gaussian linearization. The dynamic responses of the linear model have then derived analytically and validated by Monte Carlo simulations. Based on the analytical solution, a multi-objective optimization strategy is proposed for the seat suspension. The acceleration of the human body and the suspension travel are chosen as the objective indexes to evaluate seat performance. Simulation results show that the proposed optimization strategy is efficient, where a global optimum is guaranteed owing to the analytical expression of the objective function. The optimization approach taking advantage of model linearization can be applied to similar mechanical systems with friction.