This work proposes a framework to design, formulate and implement a path tracker for self-driving cars (SDC) based on a nonlinear model-predictive-control (NMPC) approach. The presented methodology is developed to be used by designers in the industrial sector, practitioners, and academics. Therefore, it is straight forward, flexible, and comprehensive. It allows the designer to easily integrate multiple objective terms in the cost function either opposing or correlating. The proposed design of the controller not only targets accurate tracking but also comfortable ride and fast travel time by introducing several sub-objective terms in the main cost function to satisfy these goals. These sub-objective terms are weighted according to their contribution to the optimization problem. The SDC-NMPC framework is developed using the high-performance language C++ and utilizes highly optimized math and optimization libraries for best real-time performance. This makes the SDC-MPC well suited for use in both ADAS and self-driving cars. Extensive simulation studies featuring complex tracks with many sharp turns have been carried out to evaluate the performance of the proposed SDC-NMPC at different speeds. The presented analysis shows that the proposed controller with its tuning technique outperforms that of the PID-based controller.

中文翻译：

---

用于自动驾驶汽车的实时 NMPC 路径跟踪器

这项工作提出了一个框架，用于设计、制定和实现基于非线性模型预测控制 (NMPC) 方法的自动驾驶汽车 (SDC) 路径跟踪器。所提出的方法被开发供工业部门的设计师、从业者和学者使用。因此，它是直接的、灵活的和全面的。它允许设计者轻松地在成本函数中集成多个目标项，无论是相反的还是相关的。通过在主要成本函数中引入几个子目标项来满足这些目标，所提出的控制器设计不仅以准确跟踪为目标，而且以舒适的乘坐体验和快速的旅行时间为目标。这些子目标项根据它们对优化问题的贡献进行加权。SDC-NMPC 框架是使用高性能语言 C++ 开发的，并利用高度优化的数学和优化库来实现最佳实时性能。这使得 SDC-MPC 非常适合用于 ADAS 和自动驾驶汽车。已经进行了具有许多急转弯的复杂轨道的广泛模拟研究，以评估所提出的 SDC-NMPC 在不同速度下的性能。所呈现的分析表明，所提出的控制器及其调谐技术优于基于 PID 的控制器。已经进行了具有许多急转弯的复杂轨道的广泛模拟研究，以评估所提出的 SDC-NMPC 在不同速度下的性能。所呈现的分析表明，所提出的控制器及其调谐技术优于基于 PID 的控制器。已经进行了具有许多急转弯的复杂轨道的广泛模拟研究，以评估所提出的 SDC-NMPC 在不同速度下的性能。所呈现的分析表明，所提出的控制器及其调谐技术优于基于 PID 的控制器。