Summary This paper deals with single-wheel suspension car model. We aim to prove the benefits of controlled semi-active suspensions compared to passive ones. The contribution relies on H 8 control ...

This paper is concerned with the synthesis of a robust gain-scheduled ℋ_∞ MIMO vehicle dynamic stability controller (VDSC) involving both steering and rear braking actuators. This VDSC aims at improving automotive vehicle yaw stability and lateral performances. The aim of this work is to provide a methodology to synthesise such a controller while taking into account the braking actuator limitations and use the steering actuator only if it is necessary. These objectives are treated in an original way by the synthesis of a parameter-dependent controller built in the LPV framework and by the solution of an LMI problem. The proposed solution is coupled with a local ABS strategy to guarantee slip stability and make the solution complete. Nonlinear time and frequency domain simulations on a complex full vehicle model (which has been validated on a real car), subject to critical driving situations, show the efficiency and robustness of the proposed solution.

The enhancement of passengers' comfort and their safety are part of the constant concerns for car manufacturers. Semiactive damping control systems have emerged to adapt the suspension features, where the road profile is one of the most important factors determining the automotive vehicle performance. Because direct measurements of the road profile represent expensive solutions and are susceptible to contamination (e.g. using laser and other visual sensors), this paper proposes a novel road profile estimator that offers the essential information (road roughness and its frequency) for the adjustment of the vehicle dynamics using conventional sensors, such as accelerometers or displacement/velocity sensors easy to mount, cheap, and useful to estimate all suspension variables. Based on the Q-parametrization approach, an adaptive observer estimates the dynamic road signal; afterward, a Fourier analysis is used to compute the road roughness condition online and to perform an International Organization for Standardization (ISO) 8608 classification. Experimental results on the rear-left corner of a 1:5 scale vehicle, equipped with electro-rheological (ER) dampers, have been used to validate the proposed road profile estimation method. Different ISO road classes evaluate the performance of the proposed algorithm, whose results show that any road can be identified successfully at least 70% of the time with a false alarm rate lower than 5%; the general accuracy of the road classifier is 95%. A second test with variable vehicle velocity shows the importance of the online frequency estimation to adapt the road estimation algorithm to any driving velocity; in this test, the road is correctly estimated in 868 of 1042 m (an error of 16.7%). Finally, the adaptability of the parametric road estimator to the semiactiveness property of the ER damper is tested at different damping coefficients.