H. Dahmani

Road curvature estimation for vehicle lane departure detection using a robust Takagi–Sugeno fuzzy observer

In this paper, a lane departure detection method is studied and evaluated via a professional vehicle dynamics software. Based on a robust fuzzy observer designed with unmeasurable premise variables with unknown inputs, the road curvature is estimated and compared with the vehicle trajectory curvature. The difference between the two curvatures is used by the proposed algorithm as the first driving risk indicator. To reduce false alarms and take into account the driver corrections, a second driving risk indicator is considered, which is based on the steering dynamics, and it gives the time to the lane keeping. The used nonlinear model deduced from the vehicle lateral dynamics and a vision system is represented by an uncertain Takagi–Sugeno fuzzy model. Taking into account the unmeasured variables, an unknown input fuzzy observer is then proposed. Synthesis conditions of the proposed fuzzy observer are formulated in terms of linear matrix inequalities using Lyapunov method. The proposed approach is evaluated under different driving scenarios using a software simulator. Simulation results show good efficiency of the proposed method.

Vehicle dynamic estimation with road bank angle consideration for rollover detection: theoretical and experimental studies

This article describes a method of vehicle dynamics estimation for impending rollover detection. This method is evaluated via a professional vehicle dynamics software and then through experimental results using a real test vehicle equipped with an inertial measurement unit. The vehicle dynamic states are estimated in the presence of the road bank angle (as a disturbance in the vehicle model) using a robust observer. The estimated roll angle and roll rate are used to compute the rollover index which is based on the prediction of the lateral load transfer. In order to anticipate the rollover detection, a new method is proposed in order to compute the time-to-rollover using the load transfer ratio. The used nonlinear model is deduced from the vehicle lateral dynamics and is represented by a Takagi–Sugeno (TS) fuzzy model. This representation is used in order to take into account the nonlinearities of lateral cornering forces. The proposed TS observer is designed with unmeasurable premise variables in order to consider the non-availability of the slip angles measurement. Simulation results show that the proposed observer and rollover detection method exhibit good efficiency.

Observer-Based Robust Control of Vehicle Dynamics for Rollover Mitigation in Critical Situations

This paper describes a vehicle dynamics fuzzy control design to improve stability and minimize the rollover risk of the vehicle in critical situations. To obtain a robust controller, several aspects that directly affect the behavior of the vehicle have been considered. Nonlinearities of the lateral forces have been considered by using a Takagi-Sugeno (TS) representation, changes in road friction have been taken into account by introducing parameter uncertainties, and, finally, the road bank angle is considered as an unknown input in the used vehicle dynamics model. A TS observer has been proposed and designed with unmeasurable premise variables in order to consider the unavailability of the sideslip angle measurement. The observer and controller gains are simultaneously obtained by solving the proposed linear matrix inequalities constraints. A fishhook test is conducted in a CarSim simulator in order to illustrate the performance of the designed controller.

Vehicle dynamics and road curvature estimation for lane departure warning system using robust fuzzy observers: experimental validation

This paper describes a new approach to estimate vehicle dynamics and the road curvature in order to detect vehicle lane departures. This method has been evaluated through an experimental set-up using a real test vehicle equipped with the RT2500 inertial measurement unit. Based on a robust unknown input fuzzy observer, the road curvature is estimated and compared to the vehicle trajectory curvature. The difference between the two curvatures is used by the proposed lane departure detection algorithm as the first driving risk indicator. To reduce false alarms and take into account driver corrections, a second driving risk indicator based on the steering dynamics is considered. The vehicle nonlinear model is deduced from the vehicle lateral dynamics and road geometry and then represented by an uncertain Takagi–Sugeno fuzzy model. Taking into account the unmeasured variables, an unknown input fuzzy observer is proposed. Synthesis conditions of the proposed fuzzy observer are formulated in terms of linear matrix inequalities using the Lyapunov method.

Fuzzy uncertain observer with unknown inputs for Lane departure detection

This paper presents a lane departure detection method. The road curvature is estimated and compared to the vehicle trajectory curvature. The proposed algorithm reduces false alarms and integrates the driver corrections by taking account of the steering dynamics. The used nonlinear model deduced from the vehicle lateral dynamics and a vision system is represented by a T-S fuzzy uncertain model with unknown inputs. Stability conditions of such observers are expressed in terms of linear matrix inequalities (LMI). Simulation results obtained in two various driving scenarios show the efficiency of the proposed method.

Observer-Based State Feedback Control for Vehicle Chassis Stability in Critical Situations

This brief describes an observer-based state feedback tracking controller for vehicle dynamics with a four-wheel active steering system as well as an active suspension system. The objective of the proposed controller is to improve the vehicle behavior by forcing the lateral dynamics and the load transfer ratio to achieve the desired vehicle behavior in critical situations. A Takagi-Sugeno (TS) representation of the lateral forces has been used in order to take the nonlinearities into account. Based on the obtained fuzzy model, a TS observer has been designed with estimated membership functions in order to consider the unavailability of the sideslip and the roll angles for measurement. Based on the Lyapunov function and the H∞ approach, the observer and controller design has been formulated in terms of Linear Matrix Inequality constraints. The proposed techniques have been evaluated through a fishhook test conducted in the CarSim professional software package.

Observer-based tracking control of the vehicle lateral dynamics using four-wheel active steering

This paper introduces an observer-based control of the vehicle lateral dynamics using a four-wheel active steering system. The Takagi-Sugeno (TS) representation has been used in order to take into account the non-linearities of the lateral forces. Based on the obtained fuzzy model, a TS observer has been designed with unmeasurable premise variables in order to consider the unavailability of the sideslip angle measurement. The objectives of the proposed controller are to improve the vehicle behavior and to force the lateral dynamics to track a reference model in critical situations. The observer and controller design has been formulated in terms of Linear Matrix Inequality (LMI) constraints using H∞ approach.

Fuzzy observer with unknown inputs using unmeasurable premise variables for vehicle dynamics and road geometry estimation

This paper describes a methodology for estimating both vehicle dynamics and road geometry using a Fuzzy unknown input observer. Vehicle sideslip and roll parameters are estimated in presence of the road bank angle and the road curvature as unknown inputs. The unknown inputs are then estimated using the observer results. The used nonlinear model deduced from the vehicle lateral and roll dynamics with a vision system is represented by a Takagi-Sugeno fuzzy model in order to take into account the nonlinearities of the cornering forces. Taking into account the unmeasured variables, an unknown inputs TS observer is then designed on the basis of the measure of the roll rate, the steering angle and the lateral offset given by the distance between the road centerline and the vehicle axe at a look-ahead distance. Synthesis conditions of the proposed fuzzy observer are formulated in terms of Linear Matrix Inequalities (LMI) using Lyapunov method. Simulation results show good efficiency of the proposed method to estimate both vehicle dynamics and road geometry.

Fuzzy observer for detection of impending vehicle rollover with road bank angle considerations

The lateral vehicle rollover represents a significant part of vehicle accidents. Driver assistance systems, previously only intended for yaw stabilization, are now being extended to incorporate rollover mitigation via braking. Preventing this kind of accident requires the knowledge of the rollover index which depends on the vehicle dynamic states and other parameters. In this paper we estimate vehicle dynamic states in presence of the road bank angle as disturbance in the vehicle model. Roll angle and roll rate are used to compute the rollover index which is based on the prediction of the lateral load transfer. The used nonlinear model deduced from the vehicle lateral and roll dynamics is represented by a Takagi-Sugeno (T-S) fuzzy model. The proposed observer is designed with unmeasurable premise variables. Stability conditions of such observers are given in terms of Linear Matrix Inequalities (LMI). The estimated states and computed rollover index are illustrated through simulation results.

Road Bank Angle Considerations for Detection of Impending Vehicle Rollover

Abstract Safety restraint systems greatly reduce the potential injury risk during vehicle accidents. One major type of accident still remains without adequate occupant protection: Vehicle Rollover. Preventing this kind of accident requires the knowledge of the rollover index which depends on the vehicle dynamic states and other parameters. In this paper we estimate vehicle dynamic states in presence of the road bank angle as disturbance in the vehicle model. Roll angle and roll rate are used to compute the rollover index which is based on the prediction of the lateral load transfer. The used nonlinear model deduced from the vehicle lateral and roll dynamics is represented by a T-S fuzzy model. Stability conditions of such observers are given in terms of Linear Matrix Inequalities (LMI). The estimated states and computed rollover index are illustrated through simulation results.