Semi-active suspension systems aim to improve the vehicle safety and comfort. For these systems control laws are required to achieve the desired performance improvements. On the other hand, the instrumentation of the vehicle suspension typically consists only in accelerometers, which are used to measure the vertical accelerations. However, velocities and/or displacements are required to implement the most common control algorithms for semi-active suspension systems. For instance, Skyhook and Groundhook controllers require the knowledge of the suspension vertical velocities. In this article several vertical velocities estimation approaches are studied and compared. In practical applications, it is common to use simple integrators to estimate these variables; nonetheless, it is well known that integrator-based estimations present errors due to drift. In applications where high performance is required, a better estimation of the state variables of the suspension system is essential. An additional problem for the estimation process is that the system is also affected by an unknown input: the road surface. This complicates the use of traditional observer schemes, thus an Unknown Input Observer (UIO) can be considered. There are many theoretical reports dealing with state estimation in presence of unknown inputs; however, only a few of them apply these techniques to vehicle suspension systems. An observer capable of estimating the unmeasured state variables of the Quarter of Vehicle (QoV) dynamics subject to unknown road surfaces is proposed. It is shown that the decoupling of the unknown input induces observability problems, which can be overcome by reducing the observer performance. A frequency-domain analysis shows that the resulting observer presents a good level of disturbance rejection of the unknown road. In addition, it is shown that the use of frequency-analysis tools can help to elucidate how to reduce the effect of the road surface even for classical observer schemes. Early results show good performance of the proposed observer in several scenarios. A comparison between several classical estimation methods is also included.
|Publication status||Published - 1 Jan 2014|
|Event||SAE Technical Papers - |
Duration: 1 Jan 2014 → …
|Conference||SAE Technical Papers|
|Period||1/1/14 → …|
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All Science Journal Classification (ASJC) codes
- Automotive Engineering
- Safety, Risk, Reliability and Quality
- Industrial and Manufacturing Engineering