Recently, model predictive control (MPC) is increasingly applied to path tracking of mobile devices, such as mobile robots. The characteristics of these MPC-based controllers are not identical due to the different approaches taken during design. According to the differences in the prediction models, we believe that the existing MPC-based path tracking controllers can be divided into four categories. We named them linear model predictive control (LMPC), linear error model predictive control (LEMPC), nonlinear model predictive control (NMPC), and nonlinear error model predictive control (NEMPC). Subsequently, we built these four controllers for the same mobile robot and compared them. By comparison, we got some conclusions. The real-time performance of LMPC and LEMPC is good, but they are less robust to reference paths and positioning errors. NMPC performs well when the reference velocity is high and the radius of the reference path is small. It is also robust to positioning errors. However, the real-time performance of NMPC is slightly worse. NEMPC has many disadvantages. Like LMPC and LEMPC, it performs poorly when the reference velocity is high and the radius of the reference path is small. Its real-time performance is also not good enough.

Path tracking of mining vehicles plays a significant role in reducing the working time of operators in the underground environment. Because the existing path tracking control of mining vehicles, based on model predictive control, is not very effective when the longitudinal velocity of the vehicle is above 2 m/s, we have devised a new controller based on nonlinear model predictive control. Then, we compare this new controller with the existing model predictive controller. In the results of our simulation, the tracking accuracy of our controller at the longitudinal velocity of 4 m/s is close to that of the existing model predictive controller, at the longitudinal velocity of 2 m/s. When longitudinal velocity is 4 m/s, the existing model predictive controller cannot drive the mining vehicle to track the given path, but our nonlinear model predictive controller can, and the maximum displacement error and heading error are 0.1382 m and 0.0589 rad, respectively. According to these results, we believe that this nonlinear model predictive controller can be used to improve the performance of the path tracking of mining vehicles.

At present, many path tracking controllers are unable to actively adjust the longitudinal velocity according to path information, such as the radius of the curve, to further improve tracking accuracy. For this problem, we propose a new path tracking framework based on model predictive control (MPC). This is a multilayer control system that includes three path tracking controllers with fixed velocities and a velocity decision controller. This new control method is named multilayer MPC. This new control method is compared to other control methods through simulation. In this paper, the maximum values of the displacement error and the heading error of multilayer MPC are 92.92% and 77.02%, respectively, smaller than those of nonlinear MPC. The real-time performance of multilayer MPC is very good, and parallel computation can further improve the real-time performance of this control method. In simulation results, the calculation time of multilayer MPC in each control period does not exceed 0.0130 s, which is much smaller than the control period. In addition, when the error of positioning systems is at the centimeter level, the performance of multilayer MPC is still good.

With the development of intelligent mining, autonomous driving will be the basic function of intelligent Load Haul Dump (LHD). Trajectory planning is the key part of autonomous driving. Due to the articulated structure, the motion state variables of the front and rear bodies are strongly nonlinear, leading to complex nonlinear collision avoidance constraints. In addition, the articulated angle and angular velocity of the LHD have physically constraints. At the same time, the terminal attitude constraint should be considered since LHD is a mining equipment. All these factors bring great difficulties to LHD trajectory planning and none of the existing methods can deal with these factors directly. In order to solve these problems, according to the most common working scenario, a novel trajectory planning method is proposed for autonomous LHD based on numerical optimization, leading to a safe and feasible time-space trajectory for efficient production. The novelty of this work is the introduction of a longitudinal and lateral trajectory planning for the typical duty cycle of LHD. More importantly, by the ingenious concept for modeling, there are two salient features of the proposed method. Firstly, no angles are used as the decision variable, and secondly, the collision constraints of the rear car are not directly considered. Through this way, the number of nonlinear constraints and the complexity of the model can keep in a reasonable level, which makes the model easy to solve. At the same time, by giving the collision-free condition and limiting the heading angular velocity, the generated trajectory is collision-free and satisfies the physical constraints of articulated angle. Case studies confirm the effectiveness of the proposed method. Adopting the proposed method to generate a spatiotemporal trajectory is beneficial to ensure safety and improve production efficiency.