No abstract

No abstract

No abstract

In order to improve the trajectory smoothness and the accuracy of lane change control, an adaptive control algorithm based on weight coefficient was proposed. According to lane change trajectory constraint conditions, the sixth-order polynomial lane change trajectory applied to intelligent vehicles was constructed. Based on the vehicle model and the model predictive control theory, the time-varying linear variable path vehicle predictive model was derived by combining soft constraint of the side slip angle. Combined with fuzzy control algorithm, the weight coefficient of the deviation of the lateral displacement was dynamically adjusted. Finally, the FMPC (model predictive controller based on fuzzy control) and MPC controller were compared and analyzed by co-simulation of CarSim and Simulink under different speeds. The simulation results show that the designed FMPC controller can track the lane change trajectory better, and the controller has better robustness when the vehicle changes lanes at different speeds.

The traditional chassis has the problems of low trafficability and poor stability under complex and changeable unstructured conditions. Thus, a wheel rail composite chassis is proposed. The chassis had a tracked travel mechanism at the front wheels and a wheeled travel mechanism at the rear wheels. This study presents the design, theoretical analysis and dynamic simulation analysis of the chassis. The maximum values of the passability of the wheel–track composite chassis that can be passed were calculated according to the relevant parameters. Furthermore, the chassis was modeled and simulated using RecurDyn to verify whether the values were reasonable. According to different values of the terrain, slope, vertical obstacle height and trench width, the change regularity of the track tension and driving torque of the chassis were obtained. The chassis is designed to improve the vehicle’s ability to operate under complex and diverse unstructured conditions.