## 1 Introduction  
Articulated vehicles are widely used in underground mining environments since an articulated vehicle's steering radius is smaller than a same-scale nonarticulated vehicle [1]. This maneuverability advantage is provided by the articulated vehicle's steering approach: the vehicle's hydraulic steering cylinders push or pull the front and rear body, making them rotate relative to each other and steer [2]. With the increasing demand for mineral resources, nearly 50 metal mines have been constructed at depths of more than 1000 m in China. Within the next ten to fifteen years, one-third of underground metal mines will enter 1000 m of mining depth [3]. Deep mining means more significant challenges, such as increased technical difficulty, harsh environments, and high safety risks [4]. As a result, the deep mining environment is unsuitable for long-term human operations. In this situation, developing autonomous mining technology is the inevitable option and necessary approach for deep resource extraction. Autonomous driving is a precondition for autonomous mining. It mainly contains three major systems: sensing and perception, path planning, and path tracking [5]. Among them, path tracking control is a critical technology to realizing autonomous driving [6]. The path tracking control aims to ensure that the vehicle tracks the planned reference trajectory accurately, stably, and robustly [7]. In recent years, a large number of path tracking algorithms have been proposed, such as pure pursuit control, Stanley tracking algorithm, proportional-integral-derivative (PID) control, sliding mode control, adaptive control, \(H_{\infty}\) control, linear quadratic regulator (LQR) control, and model-predictive control (MPC) [6; 8; 9]. MPC is increasingly used in current autonomous driving path tracking programs [10] because of its ability to handle multiple inputs and outputs and the advantage of considering system constraints [11; 12; 13]. Meanwhile, NMPC is increasingly used due to the improvement of the computational power of the controller and the update of the solution algorithm [14; 15]. Compared to passenger vehicles, there are a few studies on MPC path tracking for articulated vehicles. [PERSON] presented a switching model-predictive control scheme for an articulated vehicle under varying slip angles [16; 17; 18]. [PERSON] designed a dynamic error model-based MPC controller for steering control of an articulated vehicle [18]. [PERSON] proposed NMPC path tracking controllers for articulated vehicles. By comparing with switching model-predictive control, NMPC can be used to improve the path tracking control accuracy [19]. [PERSON] presented a novel model-based steering control for an articulated vehicle [20]. However, most of the papers mentioned above are based on simulation studies [16; 17; 18; 19]. Only [20] contains an experimental investigation but solely includes steering control. More importantly, the parameters are fixed in these MPC controllers. However, due to the nature of the work of underground articulated mining vehicles, their loads change frequently. Therefore, a specific set of controller parameters does not allow the vehicle to maintain a good tracking accuracy throughout the entire work cycle. Furthermore, the abovementioned path tracking controllers are insufficient to perform tracking experiments. Classis controllers are needed to implement the control requirements of the path tracking controller; however, there are few descriptions of the chassis wire control system for underground articulated mining vehicles. In addition, control-by-wire modifications are a prerequisite for realizing chassis control since most of the current articulated vehicles are designed to be driven manually. It is supposed to include steering-by-wire, driving-by-wire (acceleration, braking), and other actuators-by-wire systems (e.g., shoveling system, dump unloading system). For the steering-by-wire system, [PERSON] proposes four kinds of solutions [2], including:  
1.