Estimated tire forces can be used to implement unified-chassis-control (UCC) systems. This paper presents a scheme for longitudinal/lateral tire-force estimation. The longitudinal and lateral tire-force-estimation scheme has been designed, and this consists of the following five steps: vertical tire-force estimation, shaft torque estimation, longitudinal tire-force estimation based on a simplified wheel-dynamics model, lateral tire-force estimation based on a planar model, and the combined tire-force estimation. The combined tire-force-estimation scheme has been designed to compensate for the longitudinal/lateral tire-force estimator, which uses a random-walk Kalman filter. The proposed estimation scheme has been integrated into a UCC system. The performance of the UCC system, including the estimator, has been evaluated via computer simulations conducted using the vehicle dynamic software CARSIM, the ASM vehicle model, and the UCC system coded with Matlab/Simulink.

This paper presents a method for coordinated control of hybrid four-wheel drive (4WD) vehicles (H4Vs), which consists of a front internal combustion engine and independent motor-driven rear wheels. H4Vs are equipped with electronic stability control (ESC), active front steering (AFS), and 4WD. For maneuverability and lateral stability, a yaw moment controller is designed. After generating a control yaw moment with a direct yaw moment control, it is distributed with ESC, AFS, and 4WD. Several actuator configurations of ESC, AFS, and 4WD are presented in the framework of the weighted pseudo-inverse based control allocation. Simulation-based tuning is proposed to improve the performance of the yaw moment distribution. Simulations show that the proposed method is effective for the coordinated control of H4Vs for enhanced maneuverability and lateral stability.

This paper presents an MPC-based integrated control algorithm for an autonomous vehicle equipped with four-wheel independent steering and driving systems. The objective of this research is to improve the performance of the path and velocity tracking controllers by distributing the control effort to the multiple actuators. The proposed algorithm has two modules: reference state decision and MPC-based vehicle motion controller. Reference state decision module determines reference state profiles consisting of yaw rate and velocity in order to overcome the limitation of the error dynamics-based path tracking controller, which requires several assumptions on the reference path. The MPC-based vehicle motion controller is designed with a linear time-varying vehicle model in order to optimally allocate the control effort to each actuator. A linear time-varying MPC is adopted to reduce computational burden caused by using a non-linear one. The effectiveness of the proposed algorithm is validated via simulation on MATLAB/Simulink and CarSim. The simulation results show that the proposed algorithm improves the reference tracking performance by effectively distributing the control effort to the steering angle and driving force of each actuator.

For the last four decades, several steering systems for vehicles such as active front steering (AFS), front wheel independent steering (FWIS), 4-wheel steering (4WS) and 4-wheel independent steering (4WIS) have been proposed and developed. However, there have been few approaches for comparison among these steering systems with respect to yaw rate tracking or path tracking performance. This paper presents comparison among AFS, FWIS, 4WS and 4WIS in terms of vehicle stability control. In view of vehicle stability control, these systems are used as an actuator for generation of yaw moment. Direct yaw moment control is adopted to calculate a control yaw moment. Distribution from the control yaw moment into tire forces is achieved by a control allocation method. From the calculated tire forces, the steering angles of FWIS, 4WS and 4WIS are determined with a lateral tire force model. To check the performance of these actuators, simulation is conducted on vehicle simulation packages, CarSim. From the simulation, the advantages of FWIS and 4WIS are revealed over AFS and 4WS.