This paper presents a two-layer controller for accurate and robust lateral path tracking control of highly automated vehicles. The upper-layer controller, which produces the front wheel steering angle, is implemented with a Linear Time-Varying MPC (LTV-MPC) whose prediction and control horizon are both optimized offline with particle swarm optimization (PSO) under varying working conditions. A constraint on the slip angle is imposed to prevent lateral forces from saturation to guarantee vehicle stability. The lower layer is a radial basis function neural network proportion-integral-derivative (RBFNN-PID) controller that generates electric current control signals executable by the steering motor to rapidly track the target steering angle. The nonlinear characteristics of the steering system are modeled and are identified on-line with the RBFNN so that the PID controller's control parameters can be adjusted adaptively. The results of CarSim-Matlab/Simulink joint simulations show that the proposed hierarchical controller achieves a good level of path tracking accuracy while maintaining vehicle stability throughout the path tracking process, and is robust to dynamic changes in vehicle velocities and road adhesion coefficients.

中文翻译：

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用于自动车辆横向路径跟踪控制的两层控制器。

本文提出了一种两层控制器，用于对高度自动化的车辆进行精确且鲁棒的横向路径跟踪控制。产生前轮转向角的上层控制器是通过线性时变MPC（LTV-MPC）实现的，该线性时变MPC的预测和控制范围均通过粒子群优化（PSO）在不同的工作条件下进行离线优化。对滑移角施加限制以防止侧向力饱和以保证车辆稳定性。下层是径向基函数神经网络比例积分微分（RBFNN-PID）控制器，该控制器生成可由转向马达执行的电流控制信号，以快速跟踪目标转向角。对转向系统的非线性特性进行建模，并通过RBFNN在线识别，以便可以自适应地调整PID控制器的控制参数。CarSim-Matlab / Simulink联合仿真的结果表明，所提出的分层控制器在保持整个路径跟踪过程中的车辆稳定性的同时，还达到了良好的路径跟踪精度水平，并且对车辆速度和道路附着系数的动态变化具有鲁棒性。