In vehicle dynamics, yaw rate control is used to improve the cornering response in steady-state and transient conditions. This can be achieved through an appropriate anti-roll moment distribution between the front and rear axles of a vehicle with controllable suspension actuators. Such control action alters the load transfer distribution, which in turn provokes a lateral tire force variation. With respect to the extensive set of papers from the literature discussing yaw rate tracking through active suspension control, this study presents: i) A detailed analysis of the effect of the load transfer on the lateral axle force and cornering stiffness; ii) A novel linearized single-track vehicle model formulation for control system design, based on the results in i); and iii) An optimization-based routine for the design of the non-linear feedforward contribution of the control action. The resulting feedforward-feedback controller is assessed through: a) Simulations with an experimentally validated model of a vehicle with active anti-roll bars (case study 1); and b) Experimental tests on a vehicle prototype with an active suspension system (case study 2).

Rollover detection and prevention are among the most critical aspects affecting the stability and safety assessment of heavy vehicles, especially for off-road driving applications. This topic has been studied in the past and analyzed in depth in terms of vehicle modelling and control algorithms design able to prevent the rollover risk. However, it still represents a serious problem for automotive carmakers due to the huge counts among the main causes for traffic accidents. The risk also becomes more challenging to predict for off-road heavy vehicles, for which the incipient rollover might be triggered by external factors, i.e., road irregularities, bank angles as well as by aggressive input from the driver. The recent advances in road profile measurement and estimation systems make road-preview-based algorithms a viable solution for the rollover detection. This paper describes a model-based formulation to analytically evaluate the load transfer dynamics and its variation due to the presence of road perturbations, i.e., road bank angle and irregularities. An algorithm to detect and predict the rollover risk for heavy vehicles is also presented, even in presence of irregular road profiles, with the calculation of the ISO-LTR Predictive Time through the Phase-Plane analysis. Furthermore, the artificial intelligence techniques, based on the recurrent neural network approach, is also presented as a preliminary solution for a realistic implementation of the methodology. The paper finally assess the efficacy of the proposed rollover predictive algorithm by providing numerical results from the simulation of the most severe maneuvers in realistic off-road driving scenarios, also demonstrating its promising predictive capabilities.