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

TL;DR

This paper introduces a robust control method for vehicle trajectory tracking that improves performance in the presence of model parameter changes.

Contribution

The novel approach combines multi-cell hyperbody vertex modeling with double-layer optimization to enhance robustness in vehicle control.

Findings

RMPC reduces peak lateral deviation from 1.0 m to 0.2 m in complex road conditions.

The method converges heading deviation to within 2 degrees and reduces control errors compared to traditional MPC.

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.

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