Work

Improved Robust Model Predictive Trajectory Tracking Control for Intelligent Vehicles Based on Multi-Cell Hyperbody Vertex Modeling and Double-Layer Optimization

Year: 2025

Type: article

Abstract: Aiming at the problem of model parameter perturbation in vehicle trajectory tracking control, an improved robust model predictive control (RMPC) method is proposed. Based on the two-degree-of-freedom vehicle model and Serret Frenet error model, a multi-cell hypercube vertex modeling is adopted to map the disturbance range of parameters such as vehicle speed and lateral stiffness to a set of vertices, and dynamic linear combination is achieved through normalized weights. The algorithm design mainly focuses on the dual-layer optimization of the switching mechanism, decomposing the infinite time domain problem into finite time domain optimization and terminal constraints. At the same time, it dynamically updates the vertex parameters to match time-varying uncertainties and then combines Lyapunov theory to design a control invariant set. The results show that in complex road conditions and vehicle state transitions, RMPC can reduce the peak lateral deviation from 1.0 m to 0.2 m, converge the heading deviation to within 2 deg, and significantly reduce the mean and root mean square values of control errors compared to traditional MPC, under the influence of vehicle model parameter perturbations. RMPC has good robustness and real-time performance.

Source: Sensors

DOI: 10.3390/s25216537

Authors: Xiaoyu Wang, Guowei Dou, Te Chen, Jiankang Lu

Institution: Jiangsu University

Language: English

Topic: Vehicle Dynamics and Control Systems

Subfield: Automotive Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,137

Funders: National Natural Science Foundation of China, Government of Jiangsu Province