Cornering Stiffness and Sideslip Angle Estimation for Integrated Vehicle Dynamics Control

Bechtoff, Jakob; Koenig, L. A.; Isermann, Rolf

doi:10.1016/j.ifacol.2016.08.045

Cited by 32 publications

(18 citation statements)

References 9 publications

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“…A plethora of observers can be used and an appropriate combination of a tire model and an enabling observer holds the key to accurate sideslip angle estimation. The commonly used observers include EKF [18, 42–58], unscented Kalman filter (UKF) [50, 55, 59–64], sliding mode observer (SMO) [42, 44, 45, 65, 66] and the forth [67–79]. In order to further improve estimation accuracy, the underlying vehicle parameters are simultaneously estimated in some studies.…”

Section: Overview Of Sideslip Angle Estimation Methodsmentioning

confidence: 99%

“…For example, Cheli et al integrated a kinematic‐integral estimator and a dynamics‐based observer based on fuzzy logic [81]. Similarly, Bechtoff et al used the EKF to estimate sideslip angle based on conventional ESC sensors without heuristics [53]. Strano et al testified that the state‐dependent‐Riccati‐equation filter could be a valid selection as the knowledge of intrinsic parameters is not required [77].…”

Section: Overview Of Sideslip Angle Estimation Methodsmentioning

confidence: 99%

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Sideslip angle estimation of ground vehicles: a comparative study

Liu

Wang

Walker

2020

IET Control Theory & Appl

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Vehicle sideslip angle is a major indicator of dynamics stability for ground vehicles; but it is immeasurable with commercially‐available sensors. Sideslip angle estimation has been the focus of intensive research in past decades, resulting in a rich library of related literature. This study presents a comprehensive evaluation of state‐of‐the‐art sideslip angle estimation methods, with the primary goal of quantitatively revealing their strengths and limitations. These include kinematics‐, dynamics‐ and neural network‐based estimators. A hardware‐in‐loop system is purposely established to examine their performance under four typical manoeuvres. The results show that the dynamics‐based estimators are suitable at low vehicle velocities when tires operate in the linear region. In contrast, the kinematics‐based methods yield superior estimation performance at high vehicle velocities, and the inclusion of the dual GPS receivers is beneficial even when there is large disturbance to the steering angle. Of utmost importance, it is experimentally manifested that the neural network‐based estimator can perform well in all manoeuvres once the training datasets are properly selected.

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Section: Overview Of Sideslip Angle Estimation Methodsmentioning

confidence: 99%

Section: Overview Of Sideslip Angle Estimation Methodsmentioning

confidence: 99%

Sideslip angle estimation of ground vehicles: a comparative study

Liu

Wang

Walker

2020

IET Control Theory & Appl

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“…By equipping additional GPS sensors [45,46,47], an EKF-based fusion methodology integrating in-vehicle sensors and single-frequency double-antenna GPS was developed in [46] to obtain reliable estimation about vehicle state information, such as vehicle sideslip and roll angle, while EKF estimation in [47] considered the vehicle sideslip angle and TRFC by fusing measurements of GPS and IMU. In [48], the EKF with parameter adaption was investigated to estimate vehicle sideslip angle and cornering stiffness; 262 test drives validated that the estimator can deal with banked corners and varying friction coefficients.…”

Section: Model-based Vehicle State Estimationmentioning

confidence: 99%

Advanced Estimation Techniques for Vehicle System Dynamic State: A Survey

Jin

Yin

Chen

2019

Sensors

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In order to improve handling stability performance and active safety of a ground vehicle, a large number of advanced vehicle dynamics control systems—such as the direct yaw control system and active front steering system, and in particular the advanced driver assistance systems—towards connected and automated driving vehicles have recently been developed and applied. However, the practical effects and potential performance of vehicle active safety dynamics control systems heavily depend on real-time knowledge of fundamental vehicle state information, which is difficult to measure directly in a standard car because of both technical and economic reasons. This paper presents a comprehensive technical survey of the development and recent research advances in vehicle system dynamic state estimation. Different aspects of estimation strategies and methodologies in recent literature are classified into two main categories—the model-based estimation approach and the data-driven-based estimation approach. Each category is further divided into several sub-categories from the perspectives of estimation-oriented vehicle models, estimations, sensor configurations, and involved estimation techniques. The principal features of the most popular methodologies are summarized, and the pros and cons of these methodologies are also highlighted and discussed. Finally, future research directions in this field are provided.

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“…This may lead to heavy computational burden and potential risks in real-time implementation. Moreover, many vehicle parameters play an important role in vehicle dynamic, however, these parameters probably change over time, such as vehicle mass and cornering stiffness [18]. Actually, it should be noted that it is very difficult to accurately characterize the nonlinearities by existing several semiempirical tire model [19].…”

Section: Introductionmentioning

confidence: 99%

An Improved Kinematic Model Predictive Control for High-Speed Path Tracking of Autonomous Vehicles

et al. 2020

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Kinematic model predictive control (MPC) is well known for its simplicity and computational efficiency for path tracking of autonomous vehicles, however, it merely works well at low speed. In addition, earlier studies have demonstrated that tracking accuracy is improved by the feedback of yaw rate, as it improves the system transients. With this in mind, it is expected that the performance of path tracking can be improved by a cascaded controller that utilizes kinematic MPC to determine desired yaw rate rather than steering angle, and uses proportional-integral-derivative (PID) control to follow the reference yaw rate. However, directly combining MPC with PID feedback control of yaw rate results in a controller with poor tracking accuracy. The simulation results show that the cascaded MPC-PID controller has relatively stable but larger error compared to classic kinematic and dynamic MPC. Based on the analysis of vehicle sideslip angle, a novel path tracking control method is proposed, which is designed using a kinematic MPC to handle the disturbances on road curvature, a PID feedback control of yaw rate to reject uncertainties and modeling errors, and a vehicle sideslip angle compensator to correct the kinematic model prediction. The proposed controller performances involving steady-state and transient response, robustness, and computing efficiency were evaluated on Carsim/Matlab joint simulation environment. Furthermore, field experiments were conducted to validate the robustness against sensor disturbances and time lag. The results demonstrate that the developed vehicle sideslip compensator is sufficient to capture steer dynamics, and the developed controller significantly improves the performance of path tracking and follows the desired path very well, ranging from low speed to high speed even at the limits of handling. INDEX TERMS Autonomous vehicles, path tracking, lateral control, model predictive control. The associate editor coordinating the review of this manuscript and approving it for publication was Jenny Mahoney.

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Cornering Stiffness and Sideslip Angle Estimation for Integrated Vehicle Dynamics Control

Cited by 32 publications

References 9 publications

Sideslip angle estimation of ground vehicles: a comparative study

Sideslip angle estimation of ground vehicles: a comparative study

Advanced Estimation Techniques for Vehicle System Dynamic State: A Survey

An Improved Kinematic Model Predictive Control for High-Speed Path Tracking of Autonomous Vehicles

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