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

TL;DR

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.

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.

Figures (9)

• Figure 1: Schematic representation of the autonomous vehicle model and its parameters. The modeling notation depicts forces $F_{ij}$ for each tire and vehicle motion in the local reference frame $L$.
• Figure 2: Illustration of the parameter adaptive trajectory tracking and motion control framework: the LQR controller and motion planner address trajectory tracking problem and calculate the 3D phase trajectory, while three robust controllers deal with the motion control problem. LMI controller manages the longitudinal-lateral dynamics and calculates the desired slip ratio and tire side-slip angle; SMC controller manages the lateral-yaw dynamics and addresses yaw stability controller problems; BSC controller manages wheel system. The parameters in these robust controllers are adjusted by parameter adaptive strategy. RLS identifies the uncertain parameters of vehicle; GPR characterize the vehicle dynamics and calculates the boundary of model mismatch and external disturbance; Bayesian optimization determines and adjusts the above parameters which will be adopted in robust controllers.
• Figure 3: The adjusted scaling coefficients of robust boundary by Bayesian optimization.
• Figure 4: An overview of the comparative simulation framework and its relationship with robust factors and system components.
• Figure 5: The driving trajectory (a), tracking error (b) and steering angle (c) in simulation results of three controllers.