# Mass Estimation-Based Path Tracking Control for Autonomous Commercial Vehicles
## 1 Introduction  
Over the past few years, the development of autonomous driving technology has garnered significant societal attention. In scenarios like urban logistics, docks, as well as mines, it has begun to play a real role, with autonomous commercial vehicles playing an important role [1]. Autonomous driving technology stands as one of the foremost promising research areas currently [2]. Motion control is a key technology in autonomous driving, mainly divided into decoupled lateral and longitudinal control, as well as coupled lateral and longitudinal control, primarily aimed at achieving high-precision, stable, and comfortable vehicle control. Currently, most control strategies are designed based on models with certain accuracy, which can be mainly divided into kinematic models and dynamic models. Kinematicmodels primarily focus on the geometric relationships of vehicles, describing the variation rules of vehicle position and heading angle over time. Algorithms based on kinematic models, such as pure pursuit algorithms and Stanley algorithms, have good application effects in simple scenarios, but they show obvious adaptability deficiencies when dealing with roads with large curvature, making it difficult to meet the complex and variable control needs in practical applications [3]. In contrast, dynamic models can more comprehensively describe the motion characteristics of vehicles. Algorithms based on dynamic models, such as linear quadratic regulator (LQR) [4] control and model predictive control [5], theoretically provide more precise control effects. However, in actual vehicle systems, due to significant changes in vehicle parameters such as mass, delays in the vehicle actuation system, and sensor noise, challenges arise in vehicle control. Yet, the aforementioned control methods have not proposed effective solutions to these issues. To improve the precision of vehicle control, the impacts brought by the above factors need to be considered. [PERSON] [6] proposed a feedback pure pursuit control method that takes into account the estimated vehicle speed and load to adjust the preview distance. It uses lateral error as a feedback variable for compensation, improving the traditional pure pursuit control method. [PERSON] [7] proposed an adaptive Stanley control method, combined with fuzzy control, to enhance the control method's responsiveness to model deviations and external disturbances. However, it has poor path adaptability, making it unsuitable for high-speed scenarios that require rapid response. [PERSON] [8] optimized the controller using a path tracking error model. They used fuzzy logic to dynamically adjust the LQR weights, addressing the adaptability challenge of fixed weights at different vehicle speeds. However, if the model is inaccurate, the performance of the controller will significantly decrease, potentially leading to system instability or poor response. It also cannot effectively handle multiple constraint conditions. MPC can deal with optimization problems with multiple constraints, but it relies on model accuracy, is sensitive to external noise, and is prone to model mismatch issues. This is particularly prominent under varying load conditions in commercial autonomous vehicles. [PERSON] [9] proposed robust MPC control in a finite time domain. To address the model mismatch caused by parameter uncertainty, external disturbances, and model time variation, they constructed a linear parameter-varying model and solved it using linear matrix inequalities. This approach overcomes the limitations of infinite time domain MPC control, but increases the difficulty of modeling and solving. [PERSON] [10] proposed an adaptive preview MPC control that adaptively previews the future path curve, improving computational efficiency. [PERSON] [11] proposed a MPC control based on fuzzy adaptive weights that utilizes a fuzzy adaptive control algorithm to dynamically adjust the weights of the cost function within the MPC framework.