Path tracking control strategy for the intelligent vehicle considering tire nonlinear cornering characteristics in the PWA form

Sun, Xiaoyun; Wang, Yulin; Hu, Weiwei; Cai, Yingfeng; Huang, Chengbin; Chen, Long

doi:10.1016/j.jfranklin.2022.02.001

Cited by 20 publications

(15 citation statements)

References 35 publications

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“…The literature on PTC introduced the concepts of lateral offset and heading errors, denoted as e y and e φ , at a specific point C as depicted in Figure 3 . To enhance path tracking performance, this paper introduces a preview function, as used in the previous works [24,26,[30][31][32]. The preview distance, L p , is calculated using Equation ( 5), where k v is the velocity gain representing the preview interval.…”

Section: Vehicle Modelmentioning

confidence: 99%

Path Tracking Control with Constraint on Tire Slip Angles under Low-Friction Road Conditions

Lee,

Yim

2024

Applied Sciences

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This paper presents a method to design a path tracking controller with a constraint on tire slip angles under low-friction road conditions. On a low-friction road surface, a lateral tire force is easily saturated and decreases as a tire slip angle increases by a large steering angle. Under this situation, a path tracking controller cannot achieve its maximum performance. To cope with this problem, it is necessary to limit tire slip angles to a value where the maximum lateral tire force is achieved. The most commonly used controllers for path tracking, linear quadratic regulator (LQR) and model predictive control (MPC), are adopted as a controller design methodology. The control inputs of LQR and MPC are front and rear steering angles and control yaw moment, which have been widely used for path tracking. The constraint derived from tire slip angles is imposed on the steering angles of LQR and MPC. To fully verify the performance of the path tracking controller with the constraint on tire slip angles, a simulation is conducted on vehicle simulation software. From the simulation results, it is shown that the path tracking controller with the constraint on tire slip angles presented in this paper is quite effective for path tracking on low-friction road surface.

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Section: Vehicle Modelmentioning

confidence: 99%

Path Tracking Control with Constraint on Tire Slip Angles under Low-Friction Road Conditions

Lee,

Yim

2024

Applied Sciences

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“…Nowadays, numerous institutions, automotive manufacturers, and component suppliers in the AV field have devoted significant attention to developing tracking control algorithms [131]- [134]. These algorithms are primarily divided into the following three categories: 1) feedback control without prediction (e.g., proportional-integral-derivative (PID), linear quadratic regulator (LQR), and sliding mode control (SMC) [135]- [138]); 2) feedback control with the prediction (e.g., model predictive control (MPC) [30], [139]- [141]); and 3) learning-based control, such as the deep reinforcement learning.…”

Section: Trajectory Tracking Control Of Avsmentioning

confidence: 99%

A Systematic Survey of Control Techniques and Applications in Connected and Automated Vehicles

Liu,

Hua,

Deng

et al. 2023

IEEE Internet Things J.

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Vehicle control is one of the most critical challenges in autonomous vehicles (AVs) and connected and automated vehicles (CAVs), and it is paramount in vehicle safety, passenger comfort, transportation efficiency, and energy saving. This survey attempts to provide a comprehensive and thorough overview of the current state of vehicle control technology, focusing on the evolution from vehicle state estimation and trajectory tracking control in AVs at the microscopic level to collaborative control in CAVs at the macroscopic level. First, this review starts with vehicle key state estimation, specifically vehicle sideslip angle, which is the most pivotal state for vehicle trajectory control, to discuss representative approaches. Then, we present symbolic vehicle trajectory tracking control approaches for AVs. On top of that, we further review the collaborative control frameworks for CAVs and corresponding applications. Finally, this survey concludes with a discussion of future research directions and the challenges. This survey aims to provide a contextualized and indepth look at the state of the art in vehicle control for AVs and CAVs, identifying critical areas of focus and pointing out the potential areas for further exploration.

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“…In addition, the response of vehicle active safety system strongly depends on the key state parameters. Especially in extreme conditions, the common limitation of the control system is that the parameters of the vehicle model lack adaptability (Reina and Messina, 2019; Sun et al, 2022). Therefore, considering the lack of vehicle parameters adaptability and controllers multiple constraints, it is still an important open problem to effectively control vehicle path tracking and lateral stability performance.…”

Section: Introductionmentioning

confidence: 99%

Integrated control method for path tracking and lateral stability of distributed drive electric vehicles with extended Kalman filter–based tire cornering stiffness estimation

Yue

Shangguan

et al. 2023

Journal of Vibration and Control

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Aiming at the lack of adaptability of vehicle parameters under extreme conditions, this paper proposes an integrated control method for path tracking and lateral stability of distributed drive electric vehicles based on tire cornering stiffness adaptive model predictive control (MPC) scheme. The control method integrates active front steering and direct yaw control to improve the path tracking and lateral stability performance of distributed drive electric vehicles. Firstly, considering the influence of vertical load transfer, the tire cornering stiffness is estimated based on the extended Kalman filter (EKF) algorithm. Then, using this online updated tire cornering stiffness value, an adaptive MPC controller for path tracking and lateral motion stability of distributed drive electric vehicles is constructed. Meanwhile, a fuzzy sliding mode control (Fuzzy-SMC)–based longitudinal velocity controller is established to ensure the accuracy of velocity tracking. Also, according to the distributed driving characteristics of the controlled system, a tire torque distributor based on weighted pseudo-inverse (WPI) is designed with the minimum tire load rate as the optimization objective, where the road adhesion condition and the maximum output torque of the motor are considered as constraints. The simulation results show that the proposed integrated control method based on tire cornering stiffness adaptive model predictive control is robust and effective. Compared with constant cornering stiffness model predictive control–based control method, it can improve the vehicle path tracking accuracy and lateral motion stability under extreme conditions.

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Path tracking control strategy for the intelligent vehicle considering tire nonlinear cornering characteristics in the PWA form

Cited by 20 publications

References 35 publications

Path Tracking Control with Constraint on Tire Slip Angles under Low-Friction Road Conditions

Path Tracking Control with Constraint on Tire Slip Angles under Low-Friction Road Conditions

A Systematic Survey of Control Techniques and Applications in Connected and Automated Vehicles

Integrated control method for path tracking and lateral stability of distributed drive electric vehicles with extended Kalman filter–based tire cornering stiffness estimation

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