基于非线性模型预测控制的自动泊车路径跟踪

与行驶速度较高的其他无人驾驶工况相比, 自动泊车时参考路径的曲率较大, 因此车辆转向轮转角速度的限制等系统约束条件会严重影响自动泊车路径跟踪控制器的性能. 为了解决这一问题, 提出了基于非线性模型预测控制的自动泊车路径跟踪控制器, 并在MATLAB/Simulink和PreScan联合仿真环境中将该控制器与基于线性时变模型预测控制的控制器进行了对比. 仿真结果表明非线性模型预测控制器可以实现多约束条件下的自动泊车, 泊车完成后车辆航向与车位中线的夹角为0.0189 rad, 车辆后桥中点与车位中线的距离为0.1045 m, 仅为车身宽度的5.56%. 相比线性时变模型预测控制器, 非线性模型预测控制器具有泊车精度更高、安全裕度更大、泊车耗时更少等优势. 在实时性方面, 该控制器也能够满足自动泊车的需求. 

Path tracking of automatic parking based on nonlinear model predictive control

In megacities, the number of vehicles has rapidly grown. Automatic parking, a special type of unmanned driving, has become an important technology to ease parking difficulties. Path tracking is also a core part of automatic parking. However, during automatic parking, the curvature of the reference path is very large. This poses a challenge in automatic parking and is different from that in high-speed unmanned driving. When the curvature of the reference path is large, the constraints of the system severely restrain the path tracking performance. These constraints include the limit of the steering wheel angle speed. Applying model predictive control is a good way to handle multiple constraints. Recently, a path tracking controller for automatic parking based on linear time-varying model predictive control has been reported. However, for automatic parking, the accuracy of the linearized prediction model is still insufficient. To solve this problem, a path tracking controller based on nonlinear model predictive control was proposed in this paper. This controller was compared with the controller based on linear time-varying model predictive control. The simulation environment is a combination of MATLAB/Simulink and PreScan. The simulation results show that the proposed controller could complete automatic parking with multiple constraints. After the parking was completed, the angle between the vehicle heading and the center line of the parking space was 0.0189 rad. The distance between the midpoint of the rear axle of the vehicle and the center line of the parking space was 0.1045 m. This distance was only 5.56% of the width of the vehicle body. Compared with the controller based on linear time-varying model predictive control, the proposed controller for automatic parking exhibited a higher parking precision, larger safety margin, and less parking time. In terms of real-time performance, the proposed controller could also meet the requirements for automatic parking.