Trajectory Tracking of Unmanned Logistics Vehicle Based on Event-Triggered and Adaptive Optimization Parameters MPC

• Published in: Processes 2024-09, Vol.12 (9), p.1878
• Main Authors: Qiu, Jiandong, Lin, Dingqiang, Tang, Minan, Zhang, Qiang, Song, Hailong, Zhao, Zixin
• Format: Article
• Language: English
• Subjects: Adaptive control; Algorithms; Analysis; Automation; Autonomous vehicles; Cargo transportation; Communication; Control algorithms; Controllers; Cost effectiveness; Design; Disturbance observers; Fuzzy control; Fuzzy logic; Logistics; Neural networks; Nonlinear control; Optimization; Parameters; Predictive control; Robots; Smoothness; Tracking control; Tracking problem; Trajectory optimization; Unmanned aerial vehicles
• ISSN: 2227-9717; 2227-9717
• DOI: 10.3390/pr12091878

Unmanned logistics vehicle (ULV) realize the automation and intelligence of cargo transportation, which improves the efficiency, cost-effectiveness and safety of logistics and distribution, while the trajectory tracking control of ULV is the key technology to ensure their safe and efficient delivery of goods. In order to solve the trajectory tracking problem of ULV in the process of delivering goods, this paper proposes a model predictive control (MPC) method based on event-triggered and fuzzy adaptive optimization parameters. Firstly, the dynamics model of the ULV is established. Secondly, an event-triggered mechanism is introduced to establish ET-MPC, while a disturbance observer is designed considering the external disturbance and the controller calculation discarding the nonlinear term. Thirdly, the advantages of fuzzy control and MPC algorithms are integrated, and the four important parameters in the MPC controller are adaptively optimized by fuzzy control, and the improved MPC control strategy is designed. Finally, the CarSim-Matlab/Simulink co-simulation platform and the experimental vehicle platform are constructed to verify the effectiveness of the improved MPC trajectory tracking controller proposed in this paper. The results show that the improved MPC control strategy can reduce the computation time of the controller, and the total number of triggering times of the controller is reduced by 46.44% compared with the classical MPC, which reduces the computational complexity of the controller and improves the accuracy and smoothness of the trajectory tracking of the ULV.