In this study, to improve the accuracy of path tracking in intelligent vehicles, we propose an intelligent vehicle path-tracking control method based on improved model predictive control (MPC) combined with hybrid proportional-integral-derivative (PID) control theory. In the lateral control, a constraint on the side deflection of the front wheel is added based on traditional MPC and a relaxation factor is introduced to improve the stability of vehicle control for the driving stability. In longitudinal control, a hybrid PID controller is designed for different road conditions to improve the accuracy of control of vehicle speed. We present the results of a co-simulation using CarSim and MATLAB/Simulink and a test with a sample vehicle, which show that the proposed path tracking controller can greatly improve the path tracking accuracy and stability of an intelligent vehicle. The model-based prediction, rolling optimization solution, feedback control, and the addition of a constraint on the side deflection of the front wheel as well as a relaxation factor can ensure the lateral driving stability of an intelligent vehicle. The proposed approach achieved a lateral error of less than 1%, and the yaw angle was controlled within 4°. The longitudinal speed control based on hybrid PID controller can improve the response speed of the system and meet the real-time requirements of vehicle driving. 

Intelligent Vehicle Path Tracking Control Based on Improved MPC and Hybrid PID

This paper presents a new lateral control structure for path following control in an autonomous vehicle. The proposed method transforms a conventional feedback path following controller to a control architecture that relies exclusively on feedforward control of the curvature at a lookahead point of a blend path that connects the current vehicle kinematics with the desired target trajectory at some merge point. This approach provides a practical method for ensuring tracking performance and passenger comfort. Mathematical proofs and experiment results are provided to demonstrate that the proposed controller guarantees feedback stability of the lateral error dynamics, provides a systematic way of setting the corresponding feedback gains for accurate path tracking, and delivers desirable transient characteristics and smooth steering actuation effort. 

Achieving Automated Vehicle Path Following with Blend Path Curvature Control

This paper presents a new lateral control structure for path following control in an autonomous vehicle. The proposed method transforms a conventional feedback path following controller to a control architecture that relies exclusively on feedforward control of the curvature at a lookahead point of a blend path that connects the current vehicle kinematics with the desired target trajectory at some merge point. This approach provides a practical method for ensuring tracking performance and passenger comfort. Mathematical proofs and experiment results are provided to demonstrate that the proposed controller guarantees feedback stability of the lateral error dynamics, provides a systematic way of setting the corresponding feedback gains for accurate path tracking, and delivers desirable transient characteristics and smooth steering actuation effort.

In this study, to improve the accuracy of path tracking in intelligent vehicles, we propose an intelligent vehicle path-tracking control method based on improved model predictive control (MPC) combined with hybrid proportional-integral-derivative (PID) control theory. In the lateral control, a constraint on the side deflection of the front wheel is added based on traditional MPC and a relaxation factor is introduced to improve the stability of vehicle control for the driving stability. In longitudinal control, a hybrid PID controller is designed for different road conditions to improve the accuracy of control of vehicle speed. We present the results of a co-simulation using Carim and MATLAB/Simulink and a test with a sample vehicle, which show that the proposed path tracking controller can greatly improve the path tracking accuracy and stability of an intelligent vehicle. The model-based prediction, rolling optimization solution, feedback control, and the addition of a constraint on the side deflection of the front wheel as well as a relaxation factor can ensure the lateral driving stability of an intelligent vehicle. The proposed approach achieved a lateral error of less than 1%, and the yaw angle was controlled within 4°. The longitudinal speed control based on hybrid PID controller can improve the response speed of the system and meet the real-time requirements of vehicle driving.

https://ieeexplore.ieee.org/ielx7/6287639/6514899/09874852.pdf

An adaptive cascade predictive control strategy for connected and automated vehicles

Intelligent motion planning and control of automated vehicles and transportation systems have great advantages for increasing safety, energy consumption as well as driving comfort. Hereby, advanced control techniques and algorithms have to be developed meeting the challenging demands for passenger transportation. Therefore, this paper presents a cascaded (nonlinear) Model Predictive Control (MPC) structure for highly automated vehicles for position and velocity reference tracking. One focused topic is the implementation of an algorithm passing the reference trajectories information by the motion planner and the predicted optimal control signals given by the MPCs through the whole vehicle control to maintain the desired vehicle behavior down to the low-level controls. The aim of the paper is to examine the structure, tuning possibilities as well as algorithms for the connection of several MPCs for motion control. Whereby, the interface with input trajectory adaption, interpolation due to sampling time differences and reference correction is examined specifically. Presented controls and algorithms are discussed for two cascaded MPCs and compared to state of the art controls for wheel selectively drive and steered vehicles.

Analysis of a Cascaded MPC Structure for Vehicle Motion Control

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Analysis of a Cascaded MPC Structure for Vehicle Motion Control