We present a double-layered control algorithm to plan the local trajectory for automated trucks equipped with four hub motors. The main layer of the proposed control algorithm consists of a main layer nonlinear model predictive control (MLN-MPC) controller and a secondary layer nonlinear MPC (SLN-MPC) controller. The MLN-MPC controller is applied to plan a dynamically feasible trajectory, and the SLN-MPC controller is designed to limit the longitudinal slip of wheels within a stable zone to avoid the tire excessively slipping during traction. Overall, this is a closed-loop control system. Under the off-line co-simulation environments of AMESim, Simulink, dSPACE, and TruckSim, a dynamically feasible trajectory with collision avoidance operation can be generated using the proposed method, and the longitudinal wheel slip can be constrained within a stable zone so that the driving safety of the truck can be ensured under uncertain road surface conditions. In addition, the stability and robustness of the method are verified by adding a driver model to evaluate the application in the real world. Furthermore, simulation results show that there is lower computational cost compared with the conventional PID-based control method.

我们提出了一种双层控制算法，以规划配备有四个轮毂电机的自动卡车的局部轨迹。所提出的控制算法的主要层由主要层非线性模型预测控制（MLN-MPC）控制器和次要层非线性MPC（SLN-MPC）控制器组成。MLN-MPC控制器用于规划动态可行的轨迹，而SLN-MPC控制器设计用于将车轮的纵向滑动限制在稳定区域内，以避免轮胎在牵引过程中过度滑动。总的来说，这是一个闭环控制系统。在AMESim，Simulink，dSPACE和TruckSim的离线协同仿真环境下，使用所提出的方法可以生成带有防撞操作的动态可行轨迹，车轮的纵向滑移可以限制在一个稳定的区域内，从而可以在不确定的路面条件下确保卡车的行驶安全。另外，通过添加驱动程序模型来评估该方法在现实世界中的应用，验证了该方法的稳定性和鲁棒性。此外，仿真结果表明，与传统的基于PID的控制方法相比，计算成本更低。