Several studies exist on topics of semi-active suspension and vehicle cruise control systems in the literature, while many of them just consider actual road distortions and terrain characteristics, these systems are not adaptive and their subsystems designed separately. This study introduces a new method where the integration of look-ahead road data in the control of the adaptive semi-active suspension, where it is possible to the trade-off between comfort and stability orientation. This trade-off is designed by the decision layer, where the controller is modified based on prehistorical passive suspension simulations, vehicle velocity and road data, while the behavior of the controller can be modified by the use of a dedicated scheduling variable. The adaptive semi-active suspension control is designed by using Linear Parameter Varying (LPV) framework. In addition to this, it proposes designing the vehicle velocity for the cruise controller by considering energy efficiency and comfort together. TruckSim environment is used to validate the operation of the proposed integrated cruise and semi-active suspension control system.
Semi-active suspension control and vehicle cruise control systems have already been developed by researchers and adapted by automotive companies. Most of these systems react on actual road irregularities and terrain characteristics, and the control for each subsystem is designed separately. However, since oncoming road conditions can be known by using historic road information and GPS navigation system, the paper introduces a method to build in look-ahead road data in the control of the adaptive semi-active suspension, moreover, design the vehicle velocity for the cruise controller considering comfort and energy efficiency at the same time. The operation of the presented integrated suspension and velocity control system is validated by a real data simulation in TruckSim environment.
Adaptive suspension control considering passenger comfort and stability of the vehicle has been researched intensively, thus several automotive companies already apply these technologies in their high-end models. Most of these systems react to the instantaneous effects of road irregularities, however, some expensive camera-based systems adapting the suspension in coherence with upcoming road conditions have already been introduced. Thereby, using oncoming road information the performance of adaptive suspension systems can be enhanced significantly. The emerging technology of cloud computing enables several promising features for road vehicles, one of which may be the implementation of an adaptive semi-active suspension system using historic road information gathered in the cloud database. The main novelty of the paper is the developed semi-active suspension control method in which Vehicle-to-Cloud-to-Vehicle technology serves as the basis for the road adaptation capabilities of the suspension system. The semi-active suspension control is founded on the Linear Parameter-Varying framework. The operation of the presented system is validated by a real data simulation in TruckSim simulation environment.
This paper introduces an adaptive semi-active suspension control by considering global positioning system-based and historical road information. The main idea of this study is to find a corresponding trade-off between comfort and stability at different road irregularities. The introduced semi-active controller is designed based on the Linear Parameter-Varying framework. The behavior of the designed controller can be modified by the use of a scheduling variable. This scheduling variable is selected by considering the various road category. TruckSim simulation environment is used in order to validate the introduced adaptive semi-active suspension control system by comparing it with the non-adaptive scenario. The results show that both driving comfort and vehicle stability have been improved with the proposed adaptive semi-active suspension control.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.