Search Results

The accuracy of state estimation and optimal control for controllable suspension system is a challenging task for the vehicle suspension system under various road excitations. How to effectively acquire suspension states and choose the reasonable control algorithm become a hot topic in both academia and industry. Uncertainty is unavoidable for the suspension system, e.g., varying sprung or unsprung mass, suspension damping force or spring stiffness. To tackle the above problems, a novel observer-based control approach, which combines adaptive unscented Kalman filter (AUKF) observer and model predictive control (MPC), is proposed in the paper. A quarter semi-active suspension nonlinear model and road profile model are first established. Secondly, using the road classification identification method based on system response, an AUKF algorithm is employed to estimate accurately the state of suspension system.

The accuracy of chassis control for intelligent electric vehicles (IEVs), especially in road-based IEVs control for improving road holding and ride comfort, is a challenging task for the intelligent transport system. Due to the high fatality rate caused by inaccurate road-based control algorithms, how to precisely and effectively choose a reasonable road-based control algorithm become a hot topic in both academia and industry. To address and improve the performance of road holding and ride comfort of IEVs by using a semi-active suspension system, an adaptive sliding mode control (ASMC) algorithm-based road information is proposed to realize the overall performance of the intelligent vehicle chassis system in the paper. Firstly, the models of road excitation and equivalent hybrid control of a quarter semi-active suspension system are established.