This work presents proximally optimal predictive control algorithm, which is essentially a model-based lateral controller for steered autonomous vehicles that selects an optimal steering command within the neighborhood of previous steering angle based on the predicted vehicle location. The proposed algorithm was formulated with an aim of overcoming the limitations associated with the existing control laws for autonomous steering - namely PID, Pure-Pursuit and Stanley controllers. Particularly, our approach was aimed at bridging the gap between tracking efficiency and computational cost, thereby ensuring effective path tracking in real-time. The effectiveness of our approach was investigated through a series of dynamic simulation experiments pertaining to autonomous path tracking, employing an adaptive control law for longitudinal motion control of the vehicle. We measured the latency of the proposed algorithm in order to comment on its real-time factor and validated our approach by comparing it against the established control laws in terms of both crosstrack and heading errors recorded throughout the respective path tracking simulations.
翻译:这项工作提出了近似最佳的预测控制算法,基本上是一个基于模型的自主驾驶车辆横向控制器,该算法根据预测车辆位置,在先前方向方向附近选择了最佳方向指挥;拟议的算法旨在克服与自主驾驶现有控制法(即PID、Pure-Pursuit和Stanley控制器)有关的限制;特别是,我们的方法旨在弥合跟踪效率和计算成本之间的差距,从而确保实时有效跟踪路径;我们的方法的有效性通过一系列动态模拟实验进行了调查,这些实验涉及自主跟踪路径,采用适应性控制法对车辆进行长视运动控制;我们测量了拟议算法的长度,以便对其实时因素作出评论,并通过比较在交叉轨道和跟踪模拟过程中记录的所有错误方面的既定控制法,验证了我们的方法。