Research on the Model Predictive Trajectory Tracking Control of Unmanned Ground Tracked Vehicles

Abstract

This article summarizes the research significance and the development status of the unmanned ground tracked vehicles (UGTVs). According to the speed and steering principle of the UGTVs in plane motion, the kinematic state space equation of the vehicle is established. Based on the model predictive control (MPC), the UGTVs trajectory tracking controller is also established. After introducing the overall control system solution, based on the vehicle model, the track speed on both sides is used as the control input to solve the predicted output of the system. After constraining the control amount, the model prediction controller is established by using S function. Several simulations with different preset speeds are performed under linear conditions, and the numerical simulation with different prediction time domains and control time domains are performed under continuous curves. The test validates the effectiveness of the controller, and the effects of speed and time domain parameters on the deviation are analyzed based on the results. Based on satellite positioning technology, trajectory tracking tests of the UGTVs are carried out. After analyzing the positioning principle and common errors, the real-time kinematic technology is used to improve the positioning accuracy of the vehicle prototype. The hardware of the test platform includes a data acquisition system, a control execution system and a walking execution system, and the software part includes data processing and optimization control. The trajectory tracking experiments under different driving conditions are conducted, and the results show that the navigation tracking system can achieve good tracking performance.

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“…The motion control performance of UUVs can be improved in real time by using MPC. Figure 4 shows the schematic diagram of an MPC algorithm [65]. The basic idea of this type of controller is to forecast the entire system's output based on data from past and future inputs.…”

Decoupled Adaptive Motion Control for Unmanned Tracked Vehicles in the Leader-Following Task

“…However, observer-based techniques increase the complexity of the control system and exhibit poor robustness in data results. Wang et al utilized the side track velocities as control inputs and established a Model Predictive Controller (MPC) using the S-function after constraining the control variables [32]. However, due to challenges in accurately representing the tracked vehicle model, MPC methods may encounter control deviations from model inaccuracies.…”

An Optimal Hierarchical Control Strategy for 4WS-4WD Vehicles Using Nonlinear Model Predictive Control

“…This places significant demands on the control performance of trajectory tracking controllers. Scholars have undertaken numerous research investigations to produce a trajectory tracking controller that is accurate, safe, and stable for regulating autonomous driving [15][16][17][18][19][20]. Instead of considering the vehicle's own pose variation motions, such as roll and pitch, in the previously mentioned studies, researchers concentrate more on building a responsive linear vehicle model, using the vehicle's longitudinal, lateral, and transverse roll motion parameters as the controller's tracking objects.…”