On the modelling requirements for the practical implementation of advanced vehicle suspension control

This study considers the practical implementation of semi active suspension control on a test vehicle. The aim is to assess the viability of using simple models to describe the suspension state dynamics, both for simulation purposes and to enable feedback control. To this end, system identification techniques are employed to estimate model parameters, and the design of a suitable real-time observer is considered. The controller design itself, is not studied. The well known quarter-car approach is used to develop suspension models, and a simple time-domain method is presented for parameter identification. Simulated identification tests lead to the development of a new time-domain approach, based on an integrated form of the system differential equations. This is shown to have significant advantages over the direct identification method, under certain disturbance conditions. In a case study, the new method is applied to identify suspension parameters for the test vehicle, using data acquired during rig tests. Analysis of model errors then motivates a separate modelling exercise on the dampers in isolation. This yields a more complex nonlinear form of the model, which is finally validated against the simplest linear model, using measurements from the rig. For real-time state estimation, a linear Kalman Filter observer is developed. The observer's sensor requirement is examined, along with other parameters affecting the design, in a factorial experiment based on simulated and rig measured data. This allows an informed choice of the smallest sensor set that affords a high level of state estimation accuracy. The performance of the observer is also examined in the context of simulated closed-loop control. Finally, the design of an observer for the vehicle on the road is considered, and tests are carried out with the vehicle under semi-active control. Within the accuracy of state estimates derived from the available transducers, the observer performs well.