## 1 Introduction  
In the underground mining environment, ensuring the safety of operators is very important and difficult. Compared with traditional mining systems, automatic systems can reduce the working time of operators, and less working time can ensure better safety [1; 2; 3]. Automatic mining systems often include unmanned articulated vehicles, so path tracking, as a core part of unpiloted technology, plays an important role. The task of path tracking is to drive vehicles to track the given path [4; 5; 6]. Currently, model predictive control (MPC) is a widely used path tracking control method [7; 8; 9; 10; 11; 12; 13; 14; 15; 16]. The biggest advantage of MPC over other control methods, such as pure pursuit control [17], feedforward-feedback control [18], and sliding mode control [19; 20; 21; 22], is that it can take constraints of the system into account explicitly [23]. This advantage is very beneficial for solving the problem of path tracking under actuator saturation and other system constraints. Studies of the path tracking of articulated vehicles can also prove that the system constraint is not considered in control methods other than MPC. In the work of [PERSON] et al., the difference in the articulated angle speed between the actual vehicle and the model is clearly caused by system constraints [24]. [PERSON] et al. did not give control variables in their paper [25]. In the work of [PERSON] et al.,the articulated angle speed is also significantly larger than the limit of real vehicles [26]. In addition, [PERSON] et al. mentioned that not considering constraints is the weakness of their work [27]. Although there are hardware devices to prevent over-constraints in actual vehicles, frequent activation of such devices not only increases operating costs but may also cause danger. It is very advantageous to use MPC to solve this problem at the controller level. However, there are few papers on path tracking of mining vehicles based on MPC due to the special articulated steering structure of mining vehicles. A mining vehicle is shown in Figure 1. Until now, only [PERSON] et al. have devised a path tracking controller for articulated vehicles based on switching-MPC [28; 29; 30]. There is no doubt that the work of [PERSON] et al. is groundbreaking, but there are still some problems with their controller. For the MPC-based controller, the predicted error is one that the controller needs to eliminate in the future. [PERSON] et al. derives the prediction model based on an error model, thus future errors are predicted based on current errors and possible future inputs [28; 29; 30]. If the control input is assumed to be unchanged, after a sampling interval of this model, the predicted error is shown in Figure 2. When the curvature and heading of the given path are invariant, the longitudinal velocity of mining vehicles affects the reliability of predicted errors. When longitudinal velocity is slow, the reliability of predicted errors is high, as shown in Figure 2a. In contrary, when longitudinal velocity is fast, the reliability of predicted errors is low, as shown in Figure 2b. These characteristics make that [PERSON] et al. 's controller performs not good enough when the longitudinal velocity is fast. In Figure 2, \(e_{dfp}\) is the predicted displacement error and \(e_{dfr}\) is the actual future displacement error. Figure 1: Mining articulated vehicle. Figure 2: Predicted error and actual future errors when longitudinal velocity changes: (**a**) longitudinal velocity is slow; (**b**) longitudinal velocity is fast. By analyzing the researches about MPC, we find that the nonlinear model predictive control (NMPC) is more suitable to solve this problem [7; 8; 9; 10; 11; 12; 13; 14; 15; 16]. Compared to the linear time-varying model predictive control (LTV-MPC) [7; 8; 9; 10], the NMPC has a longer accurate prediction horizon [11; 12; 13; 14; 15; 16].