MPC-based path tracking with PID speed control for high-speed autonomous vehicles considering time-optimal travel

Chen, Shuping; Xiong, Guangming; Chen, Huiyan; Negrut, Dan

doi:10.1007/s11771-020-4561-1

Cited by 48 publications

(20 citation statements)

References 63 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, problems of real-time operation and robust optimization for practical applications were ignored. Shu-Ping et al [17] proposed a control mode that combined MPC and PID, which shortened the tracking time on the entire path. However, when the vehicle passed through the deceleration zone, the driving wheels left the ground for a short time.…”

Section: Introductionmentioning

confidence: 99%

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

Shi

et al. 2022

IEEE Access

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

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

Shi

et al. 2022

IEEE Access

View full text Add to dashboard Cite

show abstract

“…These methods require complex calculations, which may challenge their real-time application. To meet these requirements, Chen et al (2020) proposed a high-speed automatic driving vehicle path tracking control method based on the combination of model predictive control and PID speed control. The vehicle can obtain the most suitable steering angle in real-time.…”

Section: Introductionmentioning

confidence: 99%

An improved pure pursuit path tracking control method based on heading error rate

Wang

Chen

Wang

2022

View full text Add to dashboard Cite

Purpose In path tracking, pure pursuit (PP) has great superiority due to its simple control. However, when in agricultural applications, the performance and accuracy of PP are not so well; it cannot be tracked in time has slow convergence, and low tracking accuracy. Furthermore, in some severe driving scenarios, PP is insufficient to convey the effects of the tracking error. This paper aims to propose an autonomous driving controller to improve the PP model based on heading error rate (Improved PP-improved search strategy ant colony optimization [ISSACO]). Design/methodology/approach First, the heading error rate is added as the control method in the PP model. Second, the predicted heading error was selected as the objective function; the ISSACO is used to obtain the minimum value of the predicted heading error. A PP controller is integrated with the heading error rate by ISSACO to better deal with tracking error by trading off between PP and heading error rate. Third, the ISSACO was used to obtain the optimal values of PP and heading error rate weight. Finally, the error feedback adaptive dynamic adjustment of the improved algorithm is realized to reduce the convergence time and tracking error. Findings The proposed method was tested on a four-wheeled vehicle robot, and the effectiveness of its convergence was proved. Experiments show that the proposed method can effectively reduce the tracking error, increase convergence, then improve the robot’s working quality. Originality/value An adaptive improved PP path tracking control is proposed, which considers both heading error rate and parameter uncertainties. The new autonomous controller has a simple structure and is easy to implement. It can be adjusted according to the path tracking status to improve the adaptability of the system.

show abstract

“…Although there were defects in real time for MPC, its performance has been greatly improved in recent years, with the progress of computer performance, the improvement of algorithm structure, and the optimization of computing tools. e MPC algorithm has been widely applied to the path planning of unmanned aerial vehicles [21], autonomous vehicles [22][23][24], and unmanned surface vehicles [25].…”

Section: Introductionmentioning

confidence: 99%

Model Predictive Control-Based Collision Avoidance for Autonomous Surface Vehicles in Congested Inland Waters

Wei

Gao

2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Compared with open waters, congested inland waters have narrow waterways, many river-crossing bridges, a high density of navigation, and high current velocity in some sections. In this study, an improved collision avoidance algorithm based on model predictive control (MPC) is proposed to solve the problem of collision avoidance for autonomous surface vehicles (ASVs) in congested inland waters. First, considering the influence of current, the collision avoidance problem of ASVs is transformed into a nonlinear programming problem, and the kinematics of ASVs and the boundary of the channel are regarded as its inequality constraints. Next, since ASVs cannot perform large-scale collision avoidance in congested inland waters, the strategy of reducing the speed and slightly changing the yaw angle is adopted to realize collision avoidance. Then, an improved dynamic bumper model is used to model the safe zone of ASVs and dynamic obstacles, which improves the efficiency of the algorithm and the safety of ASVs. Finally, the collision avoidance rules and the evaluation function of the collision avoidance maneuver are constructed in the cost function of the algorithm. The simulation experiments in different encounter scenarios show that the proposed algorithm significantly improves the rationality and compliance of ASVs’ autonomous collision avoidance in congested inland waters.

show abstract

MPC-based path tracking with PID speed control for high-speed autonomous vehicles considering time-optimal travel

Cited by 48 publications

References 63 publications

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

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

An improved pure pursuit path tracking control method based on heading error rate

Model Predictive Control-Based Collision Avoidance for Autonomous Surface Vehicles in Congested Inland Waters

Contact Info

Product

Resources

About