Work

Integrated Trajectory Tracking Strategy for Unmanned Delivery Vehicles in Complex Driving Conditions

Year: 2025

Type: article

Abstract: This paper proposes an integrated trajectory tracking strategy for unmanned delivery vehicles operating under complex road geometries and varying adhesion conditions. The method combines adaptive speed regulation informed by road curvature and adhesion with lateral predictive control. A Proportional-Integral-Derivative (PID) controller is utilized for speed regulation to suppress tire force saturation. while the lateral controller adopts model predictive control (MPC) and generates steering commands by solving a quadratic programming (QP) problem with explicit constraints that cover bounds on input magnitude and input rate. Extensive co-simulations using MATLAB/Simulink and Carsim demonstrate that the proposed method outperforms traditional fixed-speed control strategies in both single- and double-lane change scenarios. It achieves superior tracking accuracy and vehicle stability and effectively suppresses sideslip and instability under low adhesion conditions. The results validate the effectiveness of the control strategy, providing key theoretical and practical insights for the safe and reliable operation of unmanned delivery vehicles in complex urban environments.

Source: Applied Sciences

DOI: 10.3390/app152111753

Authors: Haoze Chen, Jie He, Zhiming Fang, Pengcheng Qin, Changjian Zhang, Xintong Yan

Institution: Southeast University

Language: English

Topic: Vehicle Dynamics and Control Systems

Subfield: Automotive Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,896