Ali Charara

Virtual sensor: application to vehicle sideslip angle and transversal forces

This paper compares four observers (virtual sensors) of vehicle sideslip angle and lateral forces. The first is linear and uses a linear vehicle model. The remaining observers use an extended nonlinear model. The three nonlinear observers are: extended Luenberger observer, extended Kalman filter and sliding-mode observer. Modeling, model simplification, and observers are described, and an observability analysis is performed for the entire vehicle trajectory. The paper also deals with three different sets of sensors to see the impact of observers results. Comparison is first done by simulation on a valid vehicle simulator, and then observers are used on experimental data. Our study shows that observers are more accurate than simple models as regards unmeasurable variables such as sideslip angle and transversal forces. It also shows that speed of center of gravity is not an indispensable variable here.

Nonlinear control of a magnetic levitation system without premagnetization

A nonlinear model of an inertial wheel supported by active magnetic bearings is presented. A nonlinear controller based on input-output linearization is then derived to stabilize this model. This is contrasted to the typical practice of obtaining a linear model via premagnetization of the bearings through a bias current. Experimental results demonstrate that the rotor vibration and energy consumption of the bearings are lower when the nonlinear control method is used. A sliding mode control method is also proposed so that control is achieved with only two values of voltage input, in much the same manner as pulse width modulation. Simulations are also presented to test the robustness of the sliding mode control.

Evaluation of a sliding mode observer for vehicle sideslip angle

This paper presents a sliding mode observer of vehicle sideslip angle, which is the principal variable relating to the transversal forces at the tire/road interface. The vehicle is first modelled, and the model is subsequently simplified. This study validates the observer using both a validated simulator and real experimental data acquired by the Heudiasyc laboratory car, and also shows the limitations of this method. The observer requires a yaw rate sensor and data about vehicle speed are required in order to estimate sideslip angle. Some properties of the nonlinear observability matrix condition number are discussed, and relations between this variable and observation error, vehicle speed and tire cornering stiffness are presented.

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.

A comparison of external and internal methods of signal spectral analysis for broken rotor bars detection in induction motors

Like all mechanical devices, motors are subject to failures, which can sometimes lead to the shutting down of an entire industrial process. This paper looks at failure predictions in three-phase line-operated induction machines through spectral analysis or electric and electromagnetic signals. Fault characteristics frequencies generated in the estimated and the measured signal spectrum, as a result of mechanical abnormalities such as broken rotor bars, are analyzed. Spectral analyses of simple stator current, of the currents Park vector modulus, and or total and partial instantaneous electric powers are considered as external diagnosis. Internal methods of diagnosis are usually based on a mathematical model of the motor. This requires knowledge of the motors electrical parameters, which are affected by a number of physical phenomena such as temperature variations, skin effects, core losses, and saturation. As internal diagnosis, we examine different approaches to the spectral analysis of electromagnetic torque computed by stator and rotor flux estimation. To this end, the open loop method, the Luenberger observer and the Kalman filter are employed. Finally, experimental results enable us to draw up a table of comparison of internal and external methods in the detection of rotor imperfections, using two criteria under different load levels.

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.

Theoretical and experimental comparison of two nonlinear controllers for current-fed induction motors

We present here a theoretical and experimental comparison between two recent nonlinear controllers for speed regulation of current-fed induction motors: the passivity based controller (PBC) of [14] and the observer based adaptive controller (OBAC) of [9]. The theoretical comparison centers on robust stability and performance, which can be easily assessed for the PBC given its exponential stability property. On the other hand, we show that, (as expected from a scheme based on nonlinearity cancellations). in the face of parameter mismatch OBAC may become unstable even in the state feedback case. For our experimental studies we used standard low-cost hardware readily available in a practical application.

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.

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.