Daniel Lechner

Onboard Real-Time Estimation of Vehicle Lateral Tire–Road Forces and Sideslip Angle

The principal concerns in driving safety with standard vehicles or cybercars are understanding and preventing risky situations. A close examination of accident data reveals that losing control of the vehicle is the main reason for most car accidents. To help to prevent such accidents, vehicle-control systems may be used, which require certain input data concerning vehicle-dynamic parameters and vehicle-road interaction. Unfortunately, some fundamental parameters, like tire-road forces and sideslip angle are difficult to measure in a car, for both technical and economic reasons. Therefore, this study presents a dynamic modeling and observation method to estimate these variables. One of the major contributions of this study, with respect to our previous work and to the largest literature in the field of the lateral dynamic estimation, is the fact that lateral tire force at each wheel is discussed in details. To address system nonlinearities and unmodeled dynamics, two observers derived from extended and unscented Kalman filtering techniques are proposed and compared. The estimation process method is based on the dynamic response of a vehicle instrumented with available and potentially integrable sensors. Performances are tested using an experimental car. Experimental results demonstrate the ability of this approach to provide accurate estimations, and show its practical potential as a low-cost solution for calculating lateral tire forces and sideslip angle.

Experimental evaluation of observers for tire–road forces, sideslip angle and wheel cornering stiffness

This paper proposes a new estimation process to estimate tire–road forces, sideslip angle and wheel cornering stiffness. This method uses measurements from currently–available standard sensors. The estimation process is separated into two blocks: the first block contains an observer whose principal role is to calculate tire–road forces without a descriptive force model, while in the second block an observer estimates sideslip angle and cornering stiffness with an adaptive tire-force model. The different observers are based on an Extended Kalman Filter method. Concerning the vehicle model, for observability reasons, the rear longitudinal forces are neglected relative to the front longitudinal forces. The estimation process was applied and compared to real experimental data, notably wheel force measurements. Experimental results show the accuracy and potential of the estimation process, and a limitation in the estimation of the cornering stiffness.

Observers for vehicle tyre/road forces estimation: experimental validation

The motion of a vehicle is governed by the forces generated between the tyres and the road. Knowledge of these vehicle dynamic variables is important for vehicle control systems that aim to enhance vehicle stability and passenger safety. This study introduces a new estimation process for tyre/road forces. It presents many benefits over the existing state-of-art works, within the dynamic estimation framework. One of these major contributions consists of discussing in detail the vertical and lateral tyre forces at each tyre. The proposed method is based on the dynamic response of a vehicle instrumented with potentially integrated sensors. The estimation process is separated into two principal blocks. The role of the first block is to estimate vertical tyre forces, whereas in the second block two observers are proposed and compared for the estimation of lateral tyre/road forces. The different observers are based on a prediction/estimation Kalman filter. The performance of this concept is tested and compared with real experimental data using a laboratory car. Experimental results show that the proposed approach is a promising technique to provide accurate estimation. Thus, it can be considered as a practical low-cost solution for calculating vertical and lateral tyre/road forces.

Lateral load transfer and normal forces estimation for vehicle safety: experimental test

Knowledge of vehicle dynamics data is important for vehicle control systems that aim to enhance vehicle handling and passenger safety. This study introduces observers that estimate lateral load transfer and wheel–ground contact normal forces, commonly known as vertical forces. The proposed method is based on the dynamic response of a vehicle instrumented with cheap and currently available standard sensors. The estimation process is separated into three blocks: the first block serves to identify the vehicle’s mass, the second block contains a linear observer whose main role is to estimate the roll angle and the one-side lateral transfer load, while in the third block we compare linear and nonlinear models for the estimation of four wheel vertical forces. The different observers are based on a prediction/estimation filter. The performance of this concept is tested and compared with real experimental data acquired using the INRETS-MA (Institut National de Recherche sur les Transports et leur Sécurité – Département Mécanismes d’Accidents) Laboratory car. Experimental results demonstrate the ability of this approach to provide accurate estimation, thus showing its potential as a practical low-cost solution for calculating normal forces.

Estimation of road profile for vehicle dynamics motion: Experimental validation

Knowledge of vehicle dynamic data is essential for the enhancement of active safety systems such as suspensions and trajectory control systems. Vehicle controllability analysis on real roads can be obtained only if valid road profile and tire road friction model are known. With regard to the road profile, this study focuses on a real-time estimation method based on Kalman filter. Besides, this paper presents a method for calculating loads on the wheels using road profile. The proposed method is based on the dynamic response of a vehicle instrumented with available sensors. The estimation process is applied and compared to real experimental data obtained with two inertial methods in real conditions. Experimental results show the accuracy and the potential of the proposed estimation process.

An Estimation Process for Vehicle Wheel-Ground Contact Normal Forces

This paper presents a new methodology for estimating wheel-ground contact normal forces, commonly known as vertical forces. The proposed method uses measurements from currently available standard sensors (accelerometers and relative suspension sensors). The aim of this study is to improve vehicle safety, especially to prevent rollover problems. One particular feature of the method is the separation of the estimation process into three blocks. The first block serves to identify the vehicles weight, the second block contains a linear observer whose main role is to estimate the one-side lateral transfer load, while the third block calculates the four wheel vertical forces using a nonlinear observer. The different observers are based on the Kalman filter. The estimation process is applied and compared to real experimental data obtained in real conditions. Experimental results validate and prove the feasibility of this approach.

A method to estimate the lateral tire force and the sideslip angle of a vehicle: Experimental validation

The motion of a vehicle is governed by the forces generated between the tires and the road. Knowledge of these dynamic variables is important for vehicle control systems that aim to enhance vehicle stability and passenger safety. Unfortunately, it is difficult to obtain these data because of technical and economic reasons, therefore, they must be estimated. This study introduces a new estimation process for lateral tire/road forces and vehicles sideslip angle. The proposed method presents many benefits over the existing state-of-art works, within the dynamic estimation framework. One of these major contributions consists of evaluating the lateral tire forces at each tire and not per axle. The proposed estimation method is derived from the Extended Kalman filter and is based on the dynamic response of a vehicle instrumented with potentially integrable sensors. The performance of this concept is tested and compared to real experimental data using a laboratory car. Experimental results show that the proposed approach is a promising technique to provide accurate estimations of vehicle dynamic states.

Unscented Kalman filter for real-time vehicle lateral tire forces and sideslip angle estimation

Knowledge of vehicle dynamic parameters is important for vehicle control systems that aim to enhance vehicle handling and passenger safety. Unfortunately, some fundamental parameters like tire-road forces and sideslip angle are difficult to measure in a car, for both technical and economic reasons. Therefore, this study presents a dynamic modeling and observation method to estimate these variables. The ability to accurately estimate lateral tire forces and sideslip angle is a critical determinant in the performances of many vehicle control system and making vehicles danger indices. To address system nonlinearities and unmodeled dynamics, an observer derived from unscented Kalman filtering technique is proposed. The estimation process method is based on the dynamic response of a vehicle instrumented with easily-available standard sensors. Performances are tested using an experimental car in real driving situations. Experimental results show the potentiel of the estimation method.

Experimental evaluation of a sliding mode observer for tire-road forces and an extended Kalman filter for vehicle sideslip angle

This paper proposes a new process for the estimation of tire-road forces and vehicle sideslip angle. The method strictly uses measurements from sensors potentially integrable or already integrated in recent car (yaw rate, longitudinal/lateral accelerations, steering angle and angular wheel velocities). The estimation process is based on two blocks in series: the first block contains a sliding-mode observer whose principal role is to calculate tire-road forces, while in the second block an extended Kalman filter estimates sideslip angle and cornering stiffness. More specifically, this study proposes an adaptive tire-force model that takes variations in road friction into account. The paper also presents a study of convergence for the sliding-mode observer. The estimation process was applied and compared to real experimental data, in particular wheel force measurements. Experimental results show the accuracy and potential of the estimation process.

Design and experimental validation of linear and nonlinear vehicle steering control strategies

This paper proposes the design of three control laws dedicated to vehicle steering control, two based on robust linear control strategies and one based on nonlinear control strategies, and presents a comparison between them. The two robust linear control laws (indirect and direct methods) are built around M linear bicycle models, each of these control laws is composed of two M proportional integral derivative (PID) controllers: one M PID controller to control the lateral deviation and the other M PID controller to control the vehicle yaw angle. The indirect control law method is designed using an oscillation method and a nonlinear optimisation subject to H ∞ constraint. The direct control law method is designed using a linear matrix inequality optimisation in order to achieve H ∞ performances. The nonlinear control method used for the correction of the lateral deviation is based on a continuous first-order sliding-mode controller. The different methods are designed using a linear bicycle vehicle model with variant parameters, but the aim is to simulate the nonlinear vehicle behaviour under high dynamic demands with a four-wheel vehicle model. These steering vehicle controls are validated experimentally using the data acquired using a laboratory vehicle, Peugeot 307, developed by National Institute for Transport and Safety Research – Department of Accident Mechanism Analysis Laboratorys (INRETS-MA) and their performance results are compared. Moreover, an unknown input sliding-mode observer is introduced to estimate the road bank angle.