A Parameter Adaptive Trajectory Tracking and Motion Control Framework for Autonomous Vehicle

TL;DR

This paper introduces a parameter adaptive control framework for autonomous vehicles that improves trajectory tracking and yaw stability by integrating robust controllers with machine learning techniques for parameter estimation.

Contribution

It presents a modular control framework combining LQR and robust controllers with machine learning for parameter identification, enhancing robustness and reducing conservatism.

Findings

Significantly improved tracking accuracy in simulations.

Enhanced driving stability under uncertain conditions.

Robust performance maintained in extreme scenarios.

Abstract

This paper studies the trajectory tracking and motion control problems for autonomous vehicles (AVs). A parameter adaptive control framework for AVs is proposed to enhance tracking accuracy and yaw stability. While establishing linear quadratic regulator (LQR) and three robust controllers, the control framework addresses trajectory tracking and motion control in a modular fashion, without introducing complexity into each controller. The robust performance has been guaranteed in three robust controllers by considering the parameter uncertainties, mismatch of unmodeled subsystem as well as external disturbance, comprehensively. Also, the dynamic characteristics of uncertain parameters are identified by Recursive Least Squares (RLS) algorithm, while the boundaries of three robust factors are determined through combining Gaussian Process Regression (GPR) and Bayesian optimization machine learning methods, reducing the conservatism of the controller. Sufficient conditions for closed-loop stability under the diverse robust factors are provided by the Lyapunov method analytically. The simulation results on MATLAB/Simulink and Carsim joint platform demonstrate that the proposed methodology considerably improves tracking accuracy, driving stability, and robust performance, guaranteeing the feasibility and capability of driving in extreme scenarios.