Tire-road Forces Research Articles

Identification of tire forces using Dual Unscented Kalman Filter algorithm

Nowadays, application of active control systems in vehicles has been developed in order to increase safety and steerability. In these systems, using an appropriate dynamic model can be very effective in increasing the accuracy of simulations and analysis. Tire-road forces are crucial in vehicle dynamics and control since they are the only forces that a vehicle experiences from the ground and have maximum uncertainty on vehicle dynamic model. In order to simulate the non-linear regimes of vehicle motion, the ‘Pacejka’ tire model is being utilized. In this paper, a dynamic model with Dual Unscented Kalman Filter algorithm has been utilized to identify the lateral forces, side slip angle, and normal forces of tires. In order to solve the non-linear least squares problem, these parameters were given as input to the hybrid Levenberg–Marquardt and quasi Newton algorithm to find the Pacejka tire model coefficients in the offline mode. Four degrees of freedom vehicle model combined with Pacejka tire model are used for simulation in various maneuvers. Results show appropriate compatibility with CarSim software.

Estimation of lateral tire–road forces and sideslip angle for electric vehicles using interacting multiple model filter approach

Vehicle control systems require certain vehicle information (e.g., tire–road forces and vehicle sideslip angle) concerning vehicle-dynamic parameters and vehicle–road interaction, which is difficult to measure directly for both technical and economedic reasons. This paper proposes a novel method to estimate lateral tire–road forces and vehicle sideslip angle by utilizing real-time measurements. The estimation method is based on an interacting multiple model (IMM) filter that integrates in-vehicle sensors of in-wheel-motor-driven electric vehicles to adapt multiple vehicle–road system models to variable driving conditions. Based on a four-wheel nonlinear vehicle dynamics model (NVDM) considering extended roll dynamics and load transfer, the vehicle–road system model set of the IMM filter is consists of a linear tire model based NVDM and a nonlinear Dugoff tire model based NVDM. Therefore, the IMM filter can integrate the estimates from two kinds of different vehicle–road system models to improve estimation accuracy. To address system nonlinearities and un-modeled dynamics, the interacting multiple model-unscented Kalman filter (IMM-UKF) and the interacting multiple model-extended Kalman filter (IMM-EKF) are investigated and compared simultaneously. Simulation using Matlab/Simulink-Carsim is carried out to verify the effectiveness of the proposed estimation methods. The results show that the developed estimation methods can accurately estimate lateral tire–road forces and the vehicle sideslip angle.

Nonlinear tire-road friction control based on tire model parameter identification

A hierarchical control structure is a more suitable structural scheme for integrated chassis control. Generally, this type of structure has two main functions. The upper layer manages global control and force allocation, while the bottom layer allocates realized forces with 4 independent local tire controllers. The way to properly allocate these target forces poses a difficult task for the bottom layer. There are two key problems that require attention: obtaining the nonlinear time-varying coefficient of friction between the tire and different road surfaces and accurately tracking the desired forces from the upper layer. This paper mainly focuses on longitudinal tire-road friction allocation and control strategies that are based on the antilock braking system (ABS). Although it is difficult to precisely measure longitudinal tire-road friction forces for frequently changing road surface conditions, they can be estimated with a real-time measurement of brake force and angular acceleration at the wheels. The Magic Formula model is proposed as the reference model, and its key parameters are identified online using a constrained hybrid genetic algorithm to describe the evolution of tire-road friction with respect to the wheel slip. The desired wheel slip, with respect to the reference tire-road friction force from the top layer, is estimated with the inverse quadratic interpolation method. The tire-road friction controller of the extended anti-lock braking system (Ext-ABS) is designed through use of the nonlinear sliding mode control method. Simulation results indicate that acceptable modifications to changes in road surface conditions and adequate stability can be expected from the proposed control strategy.

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.

Sliding-mode-based parameter identification with application to tire pressure and tire-road friction

This paper presents a method of simultaneous estimation of tire pressure and tire-road friction. A sliding-mode scheme is designed to identify the system state and the parameter variation of a torsional tire system, which greatly depend on the change in tire pressure. Then, the recursive least-squares method with a forgetting facto is used to estimate the parameter variations of the tire system and the tire-road friction force without a friction model using the information retrieved from the equivalent input for sliding motion. A simulation study is performed to illustrate the effectiveness of the proposed method.

Nonlinear observer of sideslip angle using a particle filter estimation methodology

AbstractThe knowledge of the vehicle dynamic is important to improve the stability control of modern automotive engineering. Some key variables of the vehicle dynamic, such as the sideslip angle, are difficult to measure directly for technology or economic reasons. Lots of algorithms have been proposed to estimate these variables. To the best of our knowledge, most of these algorithms are based on linearization techniques, among which the extended Kalman filter is the most frequently used algorithm in the unmeasurable variable estimation for automotive control. In this paper, we propose two new nonlinear observers which uses the particle filter and the modified bootstrap filter to estimate the sideslip angle, respectively. These observers are based on the nonlinear double track model, in which the Dugoff model is used to describe the relation between the tire road forces and the sideslip angle. The good performances of these two observers are demonstrated by two classic experiments.

A diagnosis-based approach for tire–road forces and maximum friction estimation

A new approach to estimate vehicle tire forces and road maximum adherence is presented. Contrarily to most of the previous works on this subject, it is not an asymptotic observer-based estimation, but a combination of elementary diagnosis tools and new algebraic techniques for filtering and estimating derivatives of noisy signals. In a first step, instantaneous friction and lateral forces will be computed within this framework. Then, extended braking stiffness concept is exploited to detect which braking efforts allow to distinguish a road type from another. A weighted Dugoff model is used during these ‘distinguishable’ intervals to estimate the maximum friction coefficient. Very promising results have been obtained in noisy simulations and real experimentations for most of the driving situations.

Tire-Road Friction-Coefficient Estimation

Tire-road forces are crucial in vehicle dynamics and control because they are the only forces that a vehicle experiences from the ground. These forces significantly affect the lateral, longitudinal, yaw, and roll behavior of the vehicle. The maximum force that a tire can supply is determined by the maximum value of the tire-road friction coefficient for a given normal vertical load on the tire. For each tire, the normalized traction force <formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex>$\rho$</tex></formula> , alternatively called the coefficient of traction, is defined as

Estimation of vehicle sideslip, tire force and wheel cornering stiffness

This paper presents a process for the estimation of tire–road forces, vehicle sideslip angle and wheel cornering stiffness. The method uses measurements (yaw rate, longitudinal/lateral accelerations, steering angle and angular wheel velocities) only from sensors which can be integrated or have already been integrated in modern cars. 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. The vehicle mass is assumed to be known. Experimental results show the accuracy and potential of the estimation process.

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.

An Observer of Tire–Road Forces and Friction for Active Security Vehicle Systems

The estimation of vehicle dynamic variables is essential for the enhancement of safety, in particular for braking and trajectory-control systems. This paper proposes a new estimation process to calculate lateral tire forces, vehicle sideslip angle, and road friction. The estimation process (an adaptive observer) is constructed by combining a vehicle model and a tire force model. More specifically, this study proposes an adaptive tire force model that takes variations in road friction into account. The adaptive observer is evaluated in comparison with two nonadaptive observers that use tire force models with fixed parameters. This study also proposes a road friction identification method operating in an online context. The observers and the road friction identification method are first evaluated using vehicle simulator software. Subsequently, observers are compared to real data acquired using an experimental vehicle. This paper also includes a description of the experimental platform. Results show the accuracy and potential of the estimation process.

TMPT tire modeling in ADAMS

Because accurate modeling of the tire-road interaction forces forms the basis for virtual prototyping analysis in the field of vehicle dynamics, tire modeling has always been a key item of the Automatic Dynamic Analysis of Mechanical Systems (ADAMS) product line. This paper briefly explains the current ADAMS offering in tire modeling and the approach taken for the TMPT benchmark program. When the TMPT benchmark program is considered as a test case for vehicle dynamics tire models, ADAMS has proved to have adequate functionality in the tire/road interface and to provide the user with a wide range of tire models to pass this test successfully.

Rule Extraction with Connection Forgetting from Neural Network DYC Controller.

Recently, direct yaw moment control (DYC) using active brake has been applied on some vehicles. Its control law is determined from result of nonlinear simulation analysis. But, choosing significant inputs to DYC controller is difficult because of nonlinearity of tire-road force. The neural network (NN) can find control law of nonlinear system, but to understand the meaning is difficult because of its complex structure. In this paper, rule extraction from NN controller is presented. The NN controller is obtained by using nonlinear mathematical simulation of double lane change with genetic algorithm (GA). By addition forgetting genetic operator, the connection of NN are reduced and simplified. As a result, simple control law of DYC is achieved. The control law can be implemented by various means because of its simplicity. The DYC control law using side slip angle and time derivative of side slip angle is achieved. This control law has good performance and uses the same inputs as the DYC controller that was obtained by conventional analysis method. As a result, the effectiveness of rule extraction from NN is presented.

Extended Ground-Hook - New Concept of Semi-Active Control of Truck's Suspension

SUMMARY This paper deals with the novel control concept, so called ground-hook for active and mainly semi-active suspension of vehicles with the ultimate objective to minimize the tyre-road forces and thus the road damage. The basic ground-hook concept is extended to the several variants which enable to decrease criteria of road damage as well as to increase driver's comfort for a broad range of road unevennesses. Parameters of control law are determined by the parameter optimization for generally nonlinear model. The influence and interaction of the damping rate limits and time constants of variable shock absorbers are also taken into account. The influence of implementation of more complicated truck models is also discussed.