Nonlinear Dynamic Model Of Vehicle Research Articles

Cooperative Localization Using the 3D Euler–Lagrange Vehicle Model

Kalman filter-based cooperative localization (CL) algorithms have been shown to significantly improve pose estimations within networks of vehicles but have relied predominantly on two-dimensional kinematic models of the member agents. An inherent deficiency of the commonly employed kinematic vehicle model is the ineffectiveness of CL with only relative position measurements. In this work, we present a singularity-free CL using the full three-dimensional (3D) nonlinear dynamic vehicle model suitable for decentralized control and navigation of heterogeneous networks. We develop the algorithm, present Monte Carlo simulation results with relative pose measurements, and assess the algorithm performance as the number of measurements increases. We further demonstrate that CL with only relative position measurements is effective when using the dynamic model and benefits from increasing number of measurements. We also evaluate the performance of CL with respect to measurement task distribution, which is important in cooperative control of autonomous vehicles.

Cooperative Localization of 3D Vehicle Networks - The Dynamic Model

Cooperative localization (CL) algorithms have mainly focused on two-dimensional (2D) vehicle models and have almost uniformly employed only the kinematic model with velocities as inputs, making these algorithms unsuitable for force control problems. We have previously extended a CL algorithm to include a three-dimensional (3D) vehicle kinematic model with the use of quaternions to avoid singularities. In this paper, we extend the algorithm to a full 3D nonlinear dynamic vehicle model, again using quaternions. We evaluate the effectiveness of a CL algorithm and assess its benefits, when the full dynamic model is employed, by running a series of Monte Carlo type simulations with multiple relative pose measurements.

Intelligent momentary assisted control for autonomous emergency braking

Development of control algorithms for enhancing performance in safety-critical systems such as the Autonomous Emergency Braking (AEB) system is an important issue in the emerging field of automated electric vehicles. In this study, we model a safety distance-based hierarchical AEB control system constituted of a high-level Rule-Based Supervisory control module, an intermediate-level switching algorithm and a low-level control module. The Rule Based supervisor determines the required deceleration command that is fed to the low-level control module via the switching algorithm. In the low-level, two wheel slip control algorithms were developed, a Robust Sliding Mode control algorithm with an Artificial Neural Network (ANN) for nonlinear parameter estimation and a Gain-Scheduled Linear Quadratic Regulator. For the needs of this control design, a non-linear dynamic vehicle model was implemented whereas a constant tire-road friction coefficient was considered. The proposed control system was validated in Simulink, assuming a straight-line braking maneuver on a flat dry road. The simulation results demonstrated satisfactory emergency braking performance with full collision avoidance in both proposed control system combinations.

Distributed adaptive robust H ∞ control of intelligent‐connected electric vehicles platooning subject to communication delay

With an aim to improve the performances of intelligent-connected electric vehicle platoon with communication delay, we present a distributed adaptive robust H ∞ control scheme in this paper. First, the longitudinal non-linear dynamic model of vehicle i is established, and in order to reduce the difficulty of analysing such a non-linear system, a simplified uncertain dynamic model of vehicle i is obtained via a feedback linearisation method. Second, a novel distributed state-feedback robust platoon controller is designed by using the relative information between neighbouring electric vehicles. Third, we derive the sufficient condition that the presented distributed robust platoon controller exists under the influence of communication delay. This condition is given in the form of a linear matrix inequality, which is low dimensional and whose dimension is independent of the length of the intelligent electric vehicle platoon. Furthermore, the closed-loop platoon system controlled by the presented controller is proved stable subject to communication delay by using the Lyapunov stability theory. In addition, an adaptive control term is added to the robust platoon controller. We present the sufficient condition that such a modified controller exists and prove the platoon string stability. Finally, the effectiveness of the presented adaptive robust platoon control scheme is verified by conducting numerical simulation tests.

Adaptive Cruise Control System: Comparing LQ and MP Controllers

A vehicle longitudinal dynamics model, its inverse model and a following model are established; and then two control applications namely gain scheduling Linear Quadratic (LQ) control and Model Predictive (MP) control are designed for adaptive cruise control (ACC) system; finally an integrated Simulink model including the nonlinear dynamic vehicle model and the ACC controller (either LQ or MP) was used to test the controllers in various traffic scenarios. Comparison results between the two controllers applications is provided to show the validity of the design.

Vehicle Velocity and Roll Angle Estimation with Road and Friction Adaptation for Four-Wheel Independent Drive Electric Vehicle

Vehicle velocity and roll angle are important information for active safety control systems of four-wheel independent drive electric vehicle. In order to obtain robustness estimation of vehicle velocity and roll angle, a novel method is proposed based on vehicle dynamics and the measurement information provided by the sensors equipped in modern cars. The method is robust with respect to different road and friction conditions. Firstly, the dynamic characteristics of four-wheel independent drive electric vehicle are analyzed, and a four-degree-of-freedom nonlinear dynamic model of vehicle and a tire longitudinal dynamic equation are established. The relationship between the longitudinal and lateral friction forces is derived based on Dugoff tire model. The unknown input reconstruction technique of sliding mode observer is used to achieve longitudinal tire friction force estimation. A simple observer is designed for the estimation of the roll angle of the vehicle. And then using the relationship, the estimated longitudinal friction forces and roll angle, a sliding mode observer for vehicle velocity estimation is provided, which does not need to know the tire-road friction coefficient and road angles. Finally, the proposed method is evaluated experimentally under a variety of maneuvers and road conditions.

Parametric study of lateral stability for a railway vehicle

This paper deals with the lateral stability for a railway vehicle. Particularly, nonlinear critical speed is investigated via bifurcation analysis. Shooting method and trust region algorithm are applied to calculate the limit cycle of the nonlinear dynamic vehicle model. In case of the investigated vehicle model, the influences of the suspension parameters on the critical speeds are not large. The effects of friction coefficient are also small. However, there is an overall consistency that the nonlinear critical speed is decreased when the stiffness of the suspension is reduced. Study of the excitation conditions where the stable limit cycles occur gives very useful information to understand the nonlinear dynamic behavior of a railway vehicle and to test the vehicle with roller rig test stands. According to the results, the direction of the excitation is very important as well as the amplitude and frequency to acquire accurate nonlinear critical speed.

Stability Optimal Control Based on 15-Dof Vehicle Model

The 15 degrees of nonlinear dynamic vehicle model is established, and then Dugoff and magic formula tire model are studied by comparison. Then the braking system dynamics model and the side-slip angle estimation algorithm are discussed. Stability control system is established based on the control target of combined yaw and side-slip angle with linear 2-dof vehicle handling characteristics model. It shows that control system can perfectly control vehicle driving and can improve the stability of car active safety through the serpent’s simulation test.

Adaptive Cruise Control System: Comparing Gain-Scheduling PI and LQ Controllers

AbstractOver the recent years, a considerable growth in the number of vehicles on the road has been observed. This increases importance of vehicle safety and minimization of fuel consumption, subsequently prompting manufacturers to equip cars, with more advanced features such as adaptive cruise control (ACC)or collision avoidance and collision warning system (CWS). This paper investigates two control applications design namely the gain scheduling proportional-integral (GSPI) control and gain scheduling Linear Quadratic (GSLQ)control for ACC, covering a high range speed. The control system consist of two loops in cascade, with the inner loop controlling the vehicle speed and the outer loop switching between the cruise control (CC) and the ACC mode and calculating the reference speed. A nonlinear dynamic model of the vehicle is constructed and then a set of operating points is determined and then a of linear models is extracted in operating point. For each operating point, PI and LQ controllers are obtained off-line. An integrated Simulink model including the nonlinear dynamic vehicle model and the ACC controller (either PI or LQ) was used to test the controllers in various traffic scenarios. Comparison results between the two controllers applications is provided to show the validity of the design.

Neural speed control for autonomous road vehicles

In this paper a neural-network automotive speed controller is presented which works at low and at high speed-levels using throttle and brake control input. It can be used for autonomous intelligent cruise control including stop-and-go traffic situations. The network itself consists of a simple multilayer feedforward perceptron network. A special training method is used, where the neural network is trained on a detailed nonlinear dynamic vehicle model. Practical road tests with the Daimler Benz experimental vehicle OSCAR (MB 300 TE station wagon) show good results for both high- and low-speed control.

Relation between brinell ball tests and scleroscope readings : J. J. Thomas. ( Amer. Soc. Testing Materials, 1914)

In this paper a neural-network automotive speed controller is presented which works at low and at high speed-levels using throttle and brake control input. It can be used for autonomous intelligent cruise control including stop-and-go traffic situations. The network itself consists of a simple multilayer feedforward perceptron network. A special training method is used, where the neural network is trained on a detailed nonlinear dynamic vehicle model. Practical road tests with the Daimler Benz experimental vehicle OSCAR (MB 300 TE station wagon) show good results for both high- and low-speed control.