Influence of Model Parameters on Vehicle Suspension Control

Abstract

Numerical models are widely used to characterize the vehicle dynamics in order to control the active suspension process. However, little information is available on the evaluation of performance when the model parameters do not match real vehicle configurations. Obtaining estimates of the influence of these factors on the system control requires statistical analysis, which generates stochastic data on the issue under consideration. A sensitivity analysis of the test data is one the most successful approaches to this type of problem. A Monte Carlo simulation with uncertainty parameters for mass, front and rear stiffness and damping was used with design of experiments analysis to evaluate the performance of three methods of active suspension control (PID, MPC and LQR). In this study a sensitivity analysis was developed to determine the relevant factors and the crosscorrelation effects of their features. The methodology is applied to a model of a passenger car, which is excited by an asymmetric speed bump and uneven road profile. The changes in the behavior of the main parameters of each controller were observed and evaluated as improved for the PID and MPC controllers and worsened for the LQR controller when compared to the designed condition.