A novel control strategy is proposed for the autonomous vehicle equipped with the front and rear independent steering systems to enhance the capability of collision avoidance and improve path tracking and handling stability under critical maneuvers. The novel control system includes a path replanning controller, a path tracking controller, and a stability controller. The path replanning controller is designed to generate a collision-free trajectory by optimizing a cost function. The cost function can be updated quickly and iteratively, according to the dynamic fitting matrix and measurement signals. The path tracking controller and the stability controller with considering multi-constraints are designed to generate the steering angle of the front wheel and the rear wheel, respectively, which improve the ability of path tracking and handling stability of the autonomous vehicle under extreme conditions. The real-time effectiveness of the proposed control strategy is verified and evaluated in Hardware-in-the-loop (HIL) simulation environment. The function of collision avoidance of the control algorithm is verified in the scenario of the preset obstacle artificially on the globally planned path. The proposed control strategy, presents superior performance against two other control strategies based on model predictive control algorithm, and also exhibits satisfactory performance in terms of path tracking as well as handling stability of the autonomous vehicle.

In this paper, a new approach for an observer based controller for semi-active suspension systems is presented. The observer part is a feedback linearization Kalman filter which is based on differential geometry. The original nonlinear system is transferred to a linear system by certain coordinate transfer after the verification of observability and solvability of the system observer design problem under certain sensor placement configurations. Then, a linear Kalman filter algorithm can be applied to the linearized system. The state information can be obtained through an inverse coordinate transfer of the estimation results of the linear Kalman filter. The observer is verified by a simulation test under different road profiles, and a comparison between the designed observer and extended Kalman filter shows that the feedback linearization Kalman filter has better performance. A model reference sliding mode controller based on the estimation results of the observer is also proposed. A rig test system for the semi-active suspension system is implemented, and, both the designed observer and controller are verified through the rig test. Experimental results show that the proposed new approach for semi-active suspension control can significantly improve vehicle ride comfort with common and low-cost sensors.

A novel approach to estimate suspension state information and payload condition was developed in this article. A nonlinear quarter car model with air spring and damper was built. After verification of system observability and solvability, a certain coordinate transform was built to transform the nonlinear system into a linear one. Then a Kalman filter observer was applied. A sprung mass observer, which works cooperatively with suspension state information observer, was also designed. Designed dual-observer was verified under typical road profile and sprung mass disturbance. Compared with extended Kalman filter, the dual-observer showed better accuracy and robustness.