This paper presents a Tracking-Error Learning Control (TELC) algorithm for precise mobile robot path tracking in off-road terrain. In traditional tracking error-based control approaches, feedback and feedforward controllers are designed based on the nominal model which cannot capture the uncertainties, disturbances and changing working conditions so that they cannot ensure precise path tracking performance in the outdoor environment. In TELC algorithm, the feedforward control actions are updated by using the tracking error dynamics and the plant-model mismatch problem is thus discarded. Therefore, the feedforward controller gradually eliminates the feedback controller from the control of the system once the mobile robot has been on-track. In addition to the proof of the stability, it is proven that the cost functions do not have local minima so that the coefficients in TELC algorithm guarantee that the global minimum is reached. The experimental results show that the TELC algorithm results in better path tracking performance than the traditional tracking error-based control method. The mobile robot controlled by TELC algorithm can track a target path precisely with less than $10$ cm error in off-road terrain.
翻译:本文展示了跟踪- 错误学习控制( TELC) 的算法, 用于在越野地形跟踪精确移动机器人路径。 在传统的追踪错误控制方法中, 反馈和反馈前向控制器的设计依据是无法捕捉不确定性、 扰动和不断变化的工作条件从而无法确保准确跟踪户外环境性能的名义模型。 在 TELC 算法中, 反馈前向控制器通过使用跟踪错误动态来更新, 植物模型错配问题因此被丢弃。 因此, 进向控制器在移动机器人走上轨道后, 逐渐消除系统控制中的反馈控制器。 除了证明稳定性外, 成本功能没有本地微型功能, 从而使得TELC 算法中的系数保证达到全球最低值。 实验结果表明, TELC 算法在跟踪性能方面比传统的跟踪错误控制方法取得更好的路径跟踪结果。 由 TELC 算法控制的移动机器人可以跟踪目标路径, 准确的路径在越野地形上误差不到 10 cm 。