Dual predictive model adaptive switching control for directional control of tractor semitrailer combinations

In order to ensure that safety and reliability of tractor semitrailer combinations (TSCs) on the road, the human drivers' steering decisions need to comprehensively consider the trajectories and states of the tractor and semitrailer. For this purpose, a dual predictive model adaptive switching control decision for directional control is proposed. Firstly, a multi-point preview algorithm and a general regression neural network are designed to percept the current local target paths for the tractor and semitrailer. Then, a kinematic predictive model control algorithm for low-speed path tracking control and a dynamic predictive model control algorithm for high-speed path following and lateral stability control are established respectively. In addition, an S-type switching function is introduced to realize smooth switching between the two control algorithms. Finally, the directional control decision in this study is validated by the numerical simulations under different conditions and compared with single-point preview driver and the MPC driver without considering semitrailer. The results show that the proposed approach can accurately track the target path and effectively improve the high-speed lateral stability.