Abstract
Keywords - Motorbikes, Active Suspension, Mechatronic, Sensors, GPS
Our long-term goal is to develop a fully adaptive suspension system for motorbikes. Depending on the driving position in a (race) route the system will automatically adjust the spring rate and damping ratio. Through changes of the prestressing of the springs on the front and rare suspension a geometrical altering could be reached in some limit.
The tuning regarding the spring rate and damping ratio at front and rare wheel of a motorbike is always a compromise. On an even road the rate could be set harder to reach more transparency; on an uneven road then rather reverse. This is just an example for shortly explaining the complexity of the problematic nature.
The setting parameters of our systems will be useful for more and more popular driving training in a race route. A hobby biker will be supplied with a practical basis setup for his motorbike.
Based on the data that we have found out by our test driving we create a protocol for the particular race route. This is optimized for the tuning in each racing stages.
In the first phase of our development the biker has to set the optimized and uncompromisingly adjusted parameters, which is stored in a database, manually during the driving (semi active). The adjustable spring and damper unit would be realized for this purpose in the first phase. A corresponding control system using rapid prototyping control dSPACE would be developed too.
In the second, upgrading phase the suspension system adapts automatically to the preselected driving style such as sporty, comfortable, luggage or pillion ride. The route database supplies the setting parameters for spring and damper elements based on the actual position of the motorbike located by GPS.
We will further develop the system in the third phase so that this could work as an autarkic one.This requires sensors and observers that provide the setting parameters for self adjusting the suspension on the basis of road roughness.
The measurement data from the tree phases extend the dataset for course, road states and the suspension setting parameters accordingly. For public roads the maps of navigation systems could be supplemented through such information.
It is to expect that the database, which we have established for a certain motorbike used by testing, could be transformed to other different types of motorbikes.