Road profile is considered as an essential input that affects the vehicle dynamics. An accurate information of this data is fundamental for a better understanding of the vehicle behavior and vehicle control systems design. The present paper presents a novel algorithm (observer) suitable for real-time estimation of vertical road profile. The developed approach is based on a quarter-car model, and on elementary measurements delivered by potentially integrable sensors. The road elevation is modeled as a sinusoidal disturbance signal affecting the vehicle system. Since this signal has unknown and time-varying characteristics, the proposed estimation method implements an adaptive control scheme based on the internal model principle and on the use of the Youla-Kucera parametrization technique (also known as Q-parametrization). For performances assessment, estimations are comparatively evaluated with respect to measurements issued from the LPA (Longitudinal Profile Analyzer) profiler during experimental trials. Further, this new method is compared to the approach provided in (Doumiati et al. (2011)), where a Kalman filter is applied assuming a linear road model. Results show the validity and efficiency of the present observer scheme.     «

Road profile is considered as an essential input that affects the vehicle dynamics. An accurate information of this data is fundamental for a better understanding of the vehicle behavior and vehicle control systems design. The present paper presents a novel algorithm (observer) suitable for real-time estimation of vertical road profile. The developed approach is based on a quarter-car model, and on elementary measurements delivered by potentially integrable sensors. The road elevation is mo...     »