A study on a vehicle semi-active suspension control system based on road elevation identification

Abstract

A semi-active suspension system can effectively improve vehicle ride comfort and handling stability, and the active detection of road information is key to achieving semi-active suspension. To improve the road elevation perception ability of vehicles, this study proposes a continuous multiple scanning recursive matching algorithm based on a single-line LIDAR sensor. Radar recursive scanning is used to obtain the multiple superposition data of echo signals, and coordinate matching is realized between historical scanning data and current scanning data. Simultaneously, the sensor height deviation and pitch angle deviation of the sensors are regressed to obtain an accurate pavement elevation. Considering the control effect of the active vehicle suspension, a vehicle suspension model with seven degrees of freedom is established. The semi-active suspension controller is constructed using a diagonal recursive neural network algorithm, and the neural network weight is trained using a genetic algorithm. In addition, a preview diagonal recursive neural network control strategy for semi-active suspension, based on the combination of road elevation information, is proposed. The results of a hardware-in-the-loop co-simulation, which was conducted based on the Simulink control model and dSPACE real-time simulation, revealed that the ride comfort and stability of the vehicle were improved owing to a preview of the elevation information of the road ahead and the active adjustment of the shock absorber of the suspension system.

Research and application of a new method for adjusting the vehicle body height

“…where z 1 , z 2 and z 3 are observation values of the three state variables x 1 , x 2 and x 3 ; ŷ is the observation values of system output y, by selecting the appropriate observer gains β 1 , β 2 and β 3 , the LESO can realize the real-time tracking of the variables in the Eq (12), that is, z 1 !y, z 2 ! _ y, z 3 !…”

“…Nevertheless, semi-active suspensions achieve improvements in suspension characteristics by adjusting suspension stiffness or damping, only requiring little energy consumption, which have the advantages of simple structure and low cost [12][13][14]. If the semi-active suspension can be used to adjust the vehicle height, the vehicle height control can be realized at a lower cost and energy consumption, without installing additional vehicle body shifting devices, which has great practical application value.…”

Collision Risk Assessment for Intelligent Vehicles Considering Multi-Dimensional Uncertainties

“…Presently, risk prediction methodologies bifurcate into two streams: deterministic and probabilistic, distinguished by their consideration of future motion uncertainties. The deterministic variant typically relies on physics-driven motion models [3]- [9], such as constant velocity (CV), constant acceleration (CA), and constant yaw rate and acceleration (CYRA). These models predict vehicular trajectories and subsequently compute risk metrics like Timeto-Collision (TTC) [3], Time-to-Brake (TTB) [4], and Timeto-Steer (TTS) [5].…”

“…These models predict vehicular trajectories and subsequently compute risk metrics like Timeto-Collision (TTC) [3], Time-to-Brake (TTB) [4], and Timeto-Steer (TTS) [5]. This schema extends to the identification of potential trajectory intersections [6]- [8] or analysis of intersection duration across a given trajectory [9]- [11]. However, a deterministic paradigm has intrinsic limitations, as it fails to capture future motion uncertainties or factor in the influence of road geometry on vehicular trajectories [12].…”

Optimization of Damping in a Semi-Active Car Suspension System with Various Locations of Masses

“…The second is more comprehensive usage of preview information from camera, Global Positioning System (GPS), and electronic horizon such as vehicle-to-vehicle communication, vehicle-to-infrastructure communication, vehicle localization and real-time accessing of cloud information, and crowd sourcing leading to up-to-date road profile maps". So, there are still many areas for investigation [23,[39][40][41][42][43].…”