Abstract
Mediating the divergent interest of vehicle stability and strengthened path tracking performance when aiming at the design of a path tracking controller for autonomous vehicles is a challenging issue. Accordingly, this paper proposes an improved-LQR (linear quadratic regulator) control applied using an improved path planning algorithm. A feedforward and feedback LQR control is constructed by applying the path optimization solution method, which is a different traditional polynomial trajectory fitting method, and then solving the path planning information and the control input parameter in real time to make the tracking error as convergent as possible. To verify the superiority of the improved-LQR, this study compares the proposed controller and model predictive control by the traditional path solving method on a closed-loop test road using Carsim/Simulink. The comparative results show the efficiency, accurate tracking, vehicle stability, and reliability of the proposed controller.
Highlights
Autonomous driving has become the solution to the traffic safety and intelligent network connectivity vehicles in recent years
A lot of control approaches have been proposed in recent years, such as model predictive control (MPC), optimal preview control, and linear quadratic regulator (LQR)
Considering the fact that studies on improving the path planning algorithm of the path tracking controller are few, the main research objective of this paper lies in two aspects: (1) the path tracking controller depends on classical LQR theory, and the improved path planning algorithm is applied; and (2) under different working conditions, comparing the improved-LQR and traditional MPC on a closed test road proves the effectiveness of the improved-LQR controller; and (3) To achieve the safety research of autonomous vehicles, the safe driving envelope is applied for analyzing the simulation results
Summary
Autonomous driving has become the solution to the traffic safety and intelligent network connectivity vehicles in recent years. Path tracking has the main mission to derive the appropriate front wheel steering angle and achieve the predefined goal, which can ensure that the lateral displacement error tends to zero as soon as possible.2 To achieve this aspect, a lot of control approaches have been proposed in recent years, such as model predictive control (MPC), optimal preview control, and linear quadratic regulator (LQR). Considering the fact that studies on improving the path planning algorithm of the path tracking controller are few, the main research objective of this paper lies in two aspects: (1) the path tracking controller depends on classical LQR theory, and the improved path planning algorithm is applied; and (2) under different working conditions, comparing the improved-LQR and traditional MPC on a closed test road proves the effectiveness of the improved-LQR controller; and (3) To achieve the safety research of autonomous vehicles, the safe driving envelope is applied for analyzing the simulation results.
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