Abdelhamid Rabhi

Second order sliding-mode observer for estimation of vehicle dynamic parameters

This paper uses second-order sliding mode observers to build up an estimation scheme allowing to identify the tyre longitudinal equivalent stiffness and the effective wheel radius using the existing ABS angular sensors. This estimation strategy, based on use of the proposed observer could be used with data acquired experimentally to identify the longitudinal stiffness and effective radius of vehicle tyres. The actual results show effectiveness and robustness of the proposed method.

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.

Robust fuzzy control for stabilization of a quadrotor

In this paper, we propose an algorithm based on fuzzy control to ensure the stability of the quadrotor. After giving the nonlinear model of the robot, its representation by a Takagi-Sugeno (T-S) fuzzy model is first discussed. Next, a fuzzy controller are synthesized which guarantee desired control performances. The given controller is designed using numerical tools (Linear Matrix Inequalities-LMI). The simulation results show effectiveness and robustness 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.

Different linearization control techniques for a quadrotor system

In this paper, we propose different linearization control algorithms to solve the stabilization problem of the quadrotor. First we introduce the nonlinear model of the quadrotor. Then using tangent linearization method, a linear model is generated of the system where decentralized and centralized LQR control methods are applied. The second strategy is based on exact feedback linearization of the nonlinear model of the quadrotor. The comparison between these methods is highlighted by simulations to show effectiveness of the proposed methods.

Estimation of contact forces and tire road friction

In this paper an estimation scheme is used to built using a robust differentiator and sliding modes. This allows us to identify the tire road friction. The estimations are produced in three cascaded steps of observers and estimators. The first produces estimations of velocities and accelerations. The second estimate the tire forces and the last reconstruct the friction coefficient. The application results show effectiveness and robustness of this method.

Model-free control of a quadrotor vehicle

In this paper, the Model-Free Control (MFC) is used and tested on a Multi-Input-Multi-Output (MIMO) nonlinear system, which is a quadrotor vehicle. The system is decomposed into different-dependent Single-Input-Single-Output (SISO) sub-systems, where the MFC algorithm is applied on each one of them. MFC aims at compensating the time-varying disturbances and un-modeled system dynamics that the optimal feedback controller fails to cope with. A comparison between LQR feedback controller with and without the MFC will be implemented and tested on a real quadrotor vehicle. Different flight test results validate the importance of using the MFC, and its capability to control the quadrotor system that is using a degraded feedback controller, as well.

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.

Real-time model base fault diagnosis of PV panels using statistical signal processing

This paper proposes new method of monitoring and fault detection in photovoltaic systems, based mainly on the analysis of the power losses of the photovoltaic system (PV) by using statistical signal processing. Firstly, real time new universal circuit based model of photovoltaic panels is presented. Then, the development of software fault detection on a real installation is performed under the MATLAB/Simulink environment. With model based fault diagnosis analysis, residual signal from comparing Simulink and real model is generated. To observe clear alarm signal from arbitrary data captured, Wald test technic is applied on residual signal. A model residual based on Sequential Probability Ratio Test (WSPRT) framework for electrical fault diagnosis in PV system is introduced.

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.