State estimation of motorcycle fork : A Kalman filter, accelerometer and pressure sensor approach

Abstract: A concept for estimation of the states of a motorcycle front fork, stroke and stroke speed, have been developed utilizing a Kalman filter, pressure sensor and accelerometers. The concept development involved sensor and suspension system modeling, Kalman filter setup and tuning, sensor mounting and sensor bracket fabrication, and data recording in a dynamometer and on the road. The result shows a stroke estimation error of about ±smm when one absolute reference position is known. Difficulties arise when the temperature affect the pressure and makes the stroke estimate drift. Two solutions are proposed, one involving estimation of temperatures and compensating the drifting stroke estimate. The other solution is based on a calibration algorithm that can calibrate the system when the motorcycle is on the road. Such a calibration algorithm is proven to work according to a patent [1] and would also benefit from calibrating other sources of errors such as for example changing oil level. Two additional solutions are also investigated, namely using only accelerometers for stroke speed estimation or using accelerometers and a binary sensor that sends a pulse as the stroke reaches a certain value. Using accelerometers require double integration of the accelerometer measurements in order to obtain a value of the stroke. The integration will sum the offset in the accelerometer measurement (there will always be a small offset in a sensor) which will then will drift if no complementary sensor is used to compensate for drift. Due to that the estimation result is only useful for less than a second.