Path Tracking Control of Commercial Vehicle Emergency Obstacle Avoidance Based on MPC and Active Disturbance Rejection Control

作者：Yang Tian, He Ma, Lei Ma, Shuqiang Li, Zhenfeng Wang, Xiangyu Wang, Liang Li · 发表于：IEEE Transactions on Transportation Electrification · 年份：2024 · DOI：10.1109/tte.2024.3432898 · 被引用次数：32 · 研究领域：Vehicle Dynamics and Control Systems、Advanced Sensor and Control Systems、Simulation and Modeling Applications

If disturbances are not properly handled, autonomous driving in commercial vehicles may result in significant tracking errors or even deviate from the intended trajectory. This article proposes a solution to these issues by introducing a compensatory lateral control technique for path tracking that employs model predictive control (MPC) as its underlying control strategy. Additionally, to enhance the robustness of the MPC, the article presents a control algorithm that combines MPC with active disturbance rejection control (ADRC) compensation. Also, this article introduces the radial basis function neural network (RBFNN) as a technique for optimizing the parameters of the extended state observer (ESO) within the framework of ADRC. The introduced control strategy is evaluated via a hardware-in-the-loop (HIL) experiment, where it is demonstrated that the approach can precisely track an upper reference locus while remaining resistant to external interference.