Hocine Imine

Road profile input estimation in vehicle dynamics simulation

Vehicle motion simulation accuracy, such as in accident reconstruction or vehicle controllability analysis on real roads, can be obtained only if valid road profile and tire–road friction models are available. Regarding road profiles, a new method based on sliding mode observers has been developed and is compared with two inertial methods. Experimental results are shown and discussed to evaluate the robustness of our approach.

Sliding Mode Based Analysis and Identification of Vehicle Dynamics

Vehicles are complex mechanical systems with strong nonlinear characteristics and which can present some uncertainties due to their dynamic parameters such as masses, inertias, suspension springs, tires side slip coefficients, etc. A vehicle is composed of many parts, namely the unsprung mass, the sprung mass, the suspension which makes the link between these two masses and therefore ensures passenger comfort, and also the pneumatic which absorbs the energy coming from the road and ensures contact between the vehicle and the road. In addition to its complexity and the presence of many nonlinearities and uncertainties, the presence of some external perturbations, such as the wind and the road inputs with its own characteristics (radius of curvature, longitudinal and lateral slop, road profile and skid resistance) can cause risks not only to the vehicle but also to passengers and other road users. Many methods have been developed in order to understand the behavior of a vehicle, control it and assist the driver in order to avoid possible lane departures, rollover or jackknifing risks, to ensure a better passenger comfort by means of a suspension control and/or to estimate a safety speed and trajectory. The present book is an attempt to show how the sliding mode based observation, uncertainties identification and parameter estimation may be applied in the control of vehicle dynamics as well as for parameter and perturbations estimation. This book is the first of long series of books in the field of variable structure system in automotive application. Some other results and tools will be proposed and explained in the next publications

Steering Control for Rollover Avoidance of Heavy Vehicles

The aim of this paper is to develop an active steering assistance system to avoid the rollover of heavy vehicles (HV). The proposed approach is applied on a single body model of HV presented in this paper. An estimator based on the high-order sliding mode observer is developed to estimate the vehicle dynamics, such as lateral acceleration limit and center height of gravity. Lateral position and lateral speed are controlled using a twisting algorithm to ensure the stability of the vehicle and avoid accidents. At the same time, the identification of unsprung masses and suspension stiffness parameters of the model have been computed to increase the robustness of the method. Some simulation and experimental results are given to show the quality of the proposed concept.

Rollover Risk Prediction of Heavy Vehicle Using High-Order Sliding-Mode Observer: Experimental Results

In this paper, an original method about heavy-vehicle rollover risk prediction is presented and validated experimentally. It is based on the calculation of the load transfer ratio (LTR), which depends on the estimated vertical forces using high-order sliding-mode (HOSM) observers. Previously, a tractor model is developed. The validation tests were carried out on an instrumented tractor rolling on the road at various speeds and lane-change maneuvers. Many scenarios have been experienced: driving tests in a straight line, a curve, and a zigzag line, and brake tests to emphasize the rollover phenomenon and its prediction to set off an alarm to the driver. In this paper, the vehicle dynamic parameters (masses, inertia, stiffness, etc.) and the static-force infrastructure characteristics (road profile, radius of curvature, longitudinal and lateral slopes, and skid resistance) are measured or calculated before the tests.

Road profile inputs for evaluation of the loads on the wheels

This paper presents a method for estimating the loads on the wheels using road profiles. Regarding road profiles, a new method based on sliding mode observers has been developed and is compared with longitudinal profile analyser measurements. Experimental results are shown and discussed to evaluate the robustness of our approach.

Rollover risk prediction of an instrumented heavy vehicle using high order sliding mode observer

In this paper, an original method about heavy vehicles rollover risk prediction is presented and validated experimentally. It is based on the calculation of the LTR (Load Transfer Ratio) which depends on the estimated vertical forces using high order sliding mode observers. The validation tests were carried out on an instrumented truck rolling on the road at various speeds and lane-change manoeuvres. Many scenarios have been experienced: driving on straight line, curve line and zigzag to emphasize the rollover phenomenon and its prediction to set off an alarm to the driver.

Road profile estimation in heavy vehicle dynamics simulation

In this paper, a new method is developed in order to estimate the road profile inputs of heavy vehicle. These inputs are very important to evaluate on-board the vertical forces acting on the wheels. The proposed method is based on the use of second and third order sliding mode observers to estimate all the vehicle states (positions, speeds and accelerations) in finite time. Simulations results are given to compare the two observers and to show the robustness of the proposed method.

Identification of vehicle parameters and estimation of vertical forces

The aim of the present work is to estimate the vertical forces and to identify the unknown dynamic parameters of a vehicle using the sliding mode observers approach. The estimation of vertical forces needs a good knowledge of dynamic parameters such as damping coefficient, spring stiffness and unsprung masses, etc. In this paper, suspension stiffness and unsprung masses have been identified by the Least Square Method. Real-time tests have been carried out on an instrumented static vehicle, excited vertically by hydraulic jacks. The vehicle is equipped with different sensors in order to measure its dynamics. The measurements coming from these sensors have been considered as unknown inputs of the system. However, only the roll angle and the suspension deflection measurements have been used in order to perform the observer. Experimental results are presented and discussed to show the quality of the proposed approach.

Robust Observation of Tractor-trailer Vertical Forces Using Inverse Model and Exact Differentiator

In this paper, we are interested in developing a robust tire-force estimator for heavy duty vehicles. We use a combined model of the articulated vehicle: a yaw plane model for the chassis motion and a vertical plane model for the axles. In the proposed method, we make use of the on-board available sensors to which are added lowcost sensors. In order to optimize the sensors configuration, a robust differentiator is used in order to obtain accelerations from the measured velocities. Once the differentiation obtained, the model is inverted to determine the unknown input forces. The approach is validated by comparing the estimation results to those given by the software simulator prosper. INTRODUCTION The problem of the estimation of the contact forces for heavy duty vehicles is primordial for obtaining the necessary information on the interaction between the vehicle and the pavement. This information is particularly useful in order to avoid road and vehicle damage (tire, suspension, fragile payload) and to enhance security by preventing rollover of trucks. The issue of estimating vehicle contact forces is considered in many previous works. In [9] an Extended Kalman Filter (EKF) is applied to estimate the longitudinal and lateral forces in a vehicle bicycle model, the forces are considered as additional states and their dynamics are modeled by the use of a shaping filter driven by white noise. The authors in [11] have applied a similar estimation algorithm to an off-highway mining truck. The main limitation of this approach is that some assumptions are made on the behavior of the forces (slow variation) when tuning the shaping filter, this requires the exact knowledge of the nature of the forces and the way they vary. We can also cite [2], [8] who use sliding mode observers to estimate contact forces in heavy duty vehicles. First, the height of the wheel hub is estimated, then by measuring the road profile, the tire deflection is calculated by assuming the linear stiffness of the tire. An estimation of the vertical force is obtained by multiplying the tire deflection with its stiffness. The longitudinal and lateral forces are also derived using the Pacejka tire model. This method involves knowing accurately the vehicle and the pneumatic parameters and also to measure the road profile.

Unknown input observation via sliding modes : application to vehicle contact forces

In this paper, a method to estimate vehicle contact forces for trucks by the use of sliding mode observation tools is presented. The purpose is to use non linear observers and consider contact forces as unknown inputs for the model. We use both a bicycle model to derive the longitudinal and lateral forces and an axle model validated with a software simulator to obtain the vertical forces.