论文标题
带有汽车样拖拉机的一般2拖车的估计感知模型预测距离控制器
Estimation-aware model predictive path-following control for a general 2-trailer with a car-like tractor
论文作者
论文摘要
路径遵循控制器的设计对于可靠的自动驾驶汽车操作至关重要。由于车辆向后运动中车辆不稳定的关节角度运动学，因此对于带有汽车样拖拉机的一般2拖车的一般2拖车尤其具有挑战性。此外，已经提出了放置在拖拉机后部的高级传感器来解决关节估计问题。由于这些传感器通常具有有限的视野，因此估算解决方案引入了对接头配置的限制，可以高精度估计。为了明确考虑控制器中的这些约束，提出了一种模型预测途径跟随控制方法。提出了两种具有不同计算复杂性和性能的方法。在第一种方法中，联合角度约束被建模为凸多属的结合，因此有必要合并二进制决策变量。第二种方法以更保守的控制器为代价避免了二进制变量。在模拟和现场实验中，将提出的路径跟踪控制方法的性能与先前提出的控制策略进行了比较。
The design of the path-following controller is crucial for reliable autonomous vehicle operation. This design problem is especially challenging for a general 2-trailer with a car-like tractor due to the vehicle's unstable joint-angle kinematics in backward motion. Additionally, advanced sensors placed in the rear of the tractor have been proposed to solve the joint-angle estimation problem. Since these sensors typically have a limited field of view, the estimation solution introduces restrictions on the joint-angle configurations that can be estimated with high accuracy. To explicitly consider these constraints in the controller, a model predictive path-following control approach is proposed. Two approaches with different computation complexity and performance are presented. In the first approach, the joint-angle constraints are modeled as a union of convex polytopes, making it necessary to incorporate binary decision variables. The second approach avoids binary variables at the expense of a more conservative controller. In simulations and field experiments, the performance of the proposed path-following control approach is compared with a previously proposed control strategy.
