Simulation of preview controlled active suspension for convoy vehicles

Abstract

Designing a vehicle suspension that can give very good ride comfort along with crisp handling has been a subject for researchers for the last three decades. Research shows that active suspension can give superior performance over passive and semi-active suspension systems. Preview controlled active suspension can further improve the performance but faces the issue of sensing the road ahead of the vehicle. Previous applications of preview control have used look-ahead sensors mounted on the front bumper to measure terrain beneath. Such sensors are vulnerable, potentially confused by water, snow, or other soft obstacles; and offer a fixed preview time. For convoy vehicle applications, this thesis proposes using the overall response of the preceding vehicle(s) to generate preview controller information for follower vehicles. A robust observer is used to estimate the states of a quarter car vehicle model, from which road profile is estimated and passed on to the follower vehicle(s) to generate a preview function. The preview-active suspension, implemented in discrete time using a shift register approach to improve simulation time, reduces sprung mass acceleration and dynamic tire deflection peaks by more than 50% and 40% respectively. Terrain can change from one vehicle to the next if a loose obstacle is dislodged, or if the vehicle paths arc sufficiently different so that one vehicle misses a discrete road event. The resulting spurious preview information can give suspension performance worse than that of a passive or conventional active system In this thesis, each vehicle can effectively estimate the road profile based on its own state trajectory. By comparing its own road estimate with the preview information, preview errors can be detected and suspension control quickly switched from preview to conventional active control to preserve performance improvements compared to passive suspensions. Benefits of preview control for variation in sensor noise and more complex vehicle models must also be studied. For that, an estimation accuracy analysis has been done to study the effect of sensor noise. A half car vehicle model is also developed and discrete optimal control law is implemented to show the performance improvements. A good overall performance improvement for both the front and rear wheel is observed from the simulation results.