A Model Predictive Controller with Longitudinal Speed Compensation for Autonomous Vehicle Path Tracking

Abstract

Autonomous vehicle path tracking accuracy faces challenges in being accomplished due to the assumption that the longitudinal speed is constant in the prediction horizon in a model predictive control (MPC) control frame. A model predictive control path tracking controller with longitudinal speed compensation in the prediction horizon is proposed in this paper, which reduces the lateral deviation, course deviation, and maintains vehicle stability. The vehicle model, tire model, and path tracking model are described and linearized using the small angle approximation method and an equivalent cornering stiffness method. The mechanism of action of longitudinal speed changed with state vector variation, and the stability of the path tracking closed-loop control system in the prediction horizon is analyzed in this paper. Then the longitudinal speed compensation strategy is proposed to reduce tracking error. The controller designed was tested through simulation on the CarSim-Simulink platform, and it showed improved performance in tracking accuracy and satisfied vehicle stability constrains.

Implementation of Pure Pursuit Algorithm for Nonholonomic Mobile Robot using Robot Operating System

“…Pure pursuit algorithm (PPA) is widely used for path tracking [20]. The PPA has more simple implementation principle with better tracking results compared with other path tracking algorithms [21], [22]. PPA can be performed by using proportionalintegral-derivative (PID) algorithm [23], linear quadratic regulator algorithm [24], model predictive control algorithm [25].…”

Path Tracking Control of Autonomous Vehicle Based on Nonlinear Tire Model

“…Xu SB proposed the preview steering control algorithm and the concept of its closed-loop system analysis, and compared the model method with the MPC model based on the bicycle model [27]. Yao proposed an MPC model with longitudinal velocity compensation in the prediction range, and analyzed the longitudinal velocity change mechanism and the stability within the prediction range [28]. Sara M introduced a Tube-based MPC to consider the dynamic difference between the real vehicle and this constant nominal model, not only considering the lateral error, but also the directional error [29].…”

Control Strategies on Path Tracking for Autonomous Vehicle: State of the Art and Future Challenges

“…In recent years, with the development of artificial intelligence, big data and information processing technology, autonomous vehicles have received more and more attention. Autonomous vehicle technology aims to improve driving safety, driving comfort, and its economy, as well as reduce traffic accident rates [1][2]. Autonomous vehicle control modules mainly include environment perception and positioning, decision planning and execution control, the autonomous vehicle control system structure shown in Figure 1.…”