Integrated path tracking control based on the dimension reduction model for improving real-time performance

Abstract

In limit conditions, autonomous vehicles face the risk of lateral instability. The integrated control of steering and braking, an important measure for improving the stability of autonomous vehicles, has been extensively studied. A novel steering and braking integrated model predictive path tracking control (PTC) based on a dimension reduction model is proposed in this study. This method aims at the dilemma of the real-time limitation of the current integrated model predictive PTC based on nonlinear vehicle dynamics in practical applications and the unsatisfactory control effect of the integrated model predictive PTC based on the linearised vehicle dynamics in limit conditions. The core concept of this study is to reduce the input dimension of the integrated controller model by designing a model dimension reduction method, thereby reducing the decision variables of the optimisation problem and improving the real-time performance. The model dimension reduction method is designed based on the optimal utilisation of tire force to ensure the control performance of the proposed integrated control method in limit conditions. The integrated control method based on the dimension reduction model is compared with several existing integrated control methods in limit conditions to demonstrate its validity and superiority. The simulation tests with Simulink and CarSim indicate that the proposed method can reduce the calculation time by more than 40 $\mathrm{\%}$ on the premise of ensuring path tracking accuracy and vehicle stability in limit conditions. Moreover, the hardware-in-the-loop tests prove the practicability of the presented method.

Keywords:

• Autonomous vehicle
• path tracking control
• vehicle stability
• integrated control
• nonlinear vehicle dynamics
• model predictive control
• real-time

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The work is supported by the National Natural Science Foundation of China (52202505), the China Postdoctoral Science Foundation (2022M710354) and the Fundamental Research Funds for the Central Universities (FRF-MP-20-07, FRF-TP-20-052A1, FRF-IC-20-02).