He Zhiwei, Nie Linzhen, Yin Zhishuai, Huang Song
School of Automotive Engineering, Wuhan University of Technology, Wuhan 430070, China.
Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology, Wuhan 430070, China.
Sensors (Basel). 2020 Jul 1;20(13):3689. doi: 10.3390/s20133689.
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.
本文提出了一种用于高度自动化车辆精确且鲁棒的横向路径跟踪控制的双层控制器。产生前轮转向角的上层控制器采用线性时变模型预测控制（LTV-MPC）实现，其预测和控制时域在不同工作条件下均通过粒子群优化（PSO）进行离线优化。对侧偏角施加约束以防止侧向力饱和，从而保证车辆稳定性。下层是径向基函数神经网络比例积分微分（RBFNN-PID）控制器，它生成可由转向电机执行的电流控制信号，以快速跟踪目标转向角。利用RBFNN对转向系统的非线性特性进行建模并在线识别，从而可自适应调整PID控制器的控制参数。CarSim-Matlab/Simulink联合仿真结果表明，所提出的分层控制器在路径跟踪过程中保持车辆稳定性的同时，实现了良好的路径跟踪精度，并且对车辆速度和道路附着系数的动态变化具有鲁棒性。
