Understanding the driver of an automobile has been attractive to researchers from many different disciplines for more than half a century. On the basis of their acquirements, models of the (human) driver have been developed to better understand, analyse and improve the combined couple of driver and automobile. Due to distinctive demands on the models in accordance with different kinds of applications, a variety of driver models is available. An overview of driver models is given with respect to their application and different methodical modelling approaches. The emphasis is put on the interest of engineers, who generally focus on the automobile (like design and optimization of vehicle components and the overall vehicle dynamics behaviour) by applying their approved (mathematical) methods. Nonetheless, a brief look beyond is added to better complete the view on the involved task of driving and driver modelling for automobile dynamics application.

Driver models in automobile dynamics application

A stabilizing observer-based control algorithm for an in-wheel-motored vehicle is proposed, which generates direct yaw moment to compensate for the state deviations. The control scheme is based on a fuzzy rule-based body slip angle (beta) observer. In the design strategy of the fuzzy observer, the vehicle dynamics is represented by Takagi-Sugeno-like fuzzy models. Initially, local equivalent vehicle models are built using the linear approximations of vehicle dynamics for low and high lateral acceleration operating regimes, respectively. The optimal beta observer is then designed for each local model using Kalman filter theory. Finally, local observers are combined to form the overall control system by using fuzzy rules. These fuzzy rules represent the qualitative relationships among the variables associated with the nonlinear and uncertain nature of vehicle dynamics, such as tire force saturation and the influence of road adherence. An adaptation mechanism for the fuzzy membership functions has been incorporated to improve the accuracy and performance of the system. The effectiveness of this design approach has been demonstrated in simulations and in a real-time experimental setting.

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Direct Yaw-Moment Control of an In-Wheel-Motored Electric Vehicle Based on Body Slip Angle Fuzzy Observer

Precision fish farming: A new framework to improve production in aquaculture

Over the last twenty to thirty years, railway vehicle dynamics has changed from being an essentially mechanical engineering discipline to one that is increasingly starting to include sensors, electronics and computer processing. This paper surveys the application of these technologies to suspensions and running gear, focused upon the complementary issues of control (which has been reviewed within Vehicle System Dynamics previously) and monitoring (which has not previously been reviewed). The theory, concepts and implementation status are assessed in each case, from which the paper identifies the key trends and concludes with a forward look at what is likely to develop over the next years.

Control and monitoring for railway vehicle dynamics

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.

Onboard Real-Time Estimation of Vehicle Lateral Tire–Road Forces and Sideslip Angle

The article demonstrates the implementation of a model-based vehicle estimator, which can be used for combined estimation of vehicle states and parameters. The estimator is realised using the dual extended Kalman filter (DEKF) technique, which makes use of two Kalman filters running in parallel, thus ‘splitting’ the state and parameter estimation problems. Note that the two problems cannot be entirely separated due to their inherent interdependencies. This technique provides several advantages, such as the possibility to switch off the parameter estimator, once a sufficiently good set of estimates has been obtained. The estimator is based on a four-wheel vehicle model with four degrees of freedom, which accommodates the dominant modes only, and is designed to make use of several interchangeable tyre models. The paper demonstrates the appropriateness of the DEKF. Results to date indicate that this is an effective approach, which is considered to be of potential benefit to the automotive industry.

/pdf/dual-extended-kalman-filter-for-vehicle-state-and-parameter-3fqhya7p26.pdf

Dual extended Kalman filter for vehicle state and parameter estimation

This paper demonstrates the use of an extended Kalman filter (KF) as a virtual sensor for non-measurable vehicle states and unknown vehicle parameters The purpose of obtaining these values is to make them available within the control algorithms of the various automotive stability systems Based on an extensive four-wheel vehicle model, an estimator is implemented on data from a test vehicle Using available reference data, the suitability of the extended KF technique as a virtual sensor is demonstrated

Kalman filter as a virtual sensor: applied to automotive stability systems:

This paper demonstrates the implementation of a comprehensive vehicle model combined with a driver model in closed loop for the purpose of developing and testing of vehicle stability systems. As an example of such a stability system, a model of an active front steering controller is considered. The operation of this controller is based on vehicle states available my means of a state estimator. The functional efficiency of these combined models is demonstrated using a double-lane change manoeuvre. The use of the stability controller shows clear improvement in terms of the vehicle dynamical stability.

Closed-loop driver/vehicle model for automotive control

It is extremely difficult to maintain simultaneously a high standard of ride, handling, and body control in a vehicle with a conventional passive suspension. However, it is well known that ...

http://journals.sagepub.com/doi/pdf/10.1243/095965105X77501

Control of a hydropneumatic active suspension based on a non-linear quarter-car model

This paper describes the development of a hybrid approach to estimate the states and parameters of a vehicle. The parameter estimator is based on a genetic algorithm in conjunction with a bank of extended Kalman filters, which are simultaneously utilised to estimate the states of the system. This paper shows results of the approach when applied to a four-wheel-vehicle model and the advantages and limitations are discussed.

R.A. Williams

Papers

Dual extended Kalman filter for vehicle state and parameter estimation

Kalman filter as a virtual sensor: applied to automotive stability systems:

Closed-loop driver/vehicle model for automotive control

Control of a hydropneumatic active suspension based on a non-linear quarter-car model

Hybrid genetic algorithms / extended kalman filter approach for vehicle state and parameter estimation