Non-Linear Analysis of Vehicle Dynamics (NAVDyn): A Reduced Order Model for Vehicle Handling Analysis

Demerly, Jon D.

doi:10.4271/2000-01-1621

Cited by 21 publications

(14 citation statements)

References 5 publications

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“…The CarSim vehicle model, a high-order model covering all degrees of freedom (18 DOF) of a vehicle, is a commercially available product with a well-dened user interface that can verify the accuracy of a built model. [10]. This model consisted of longitudinal and lateral velocities originating from the chassis, the vertical body roll angle, the vehicle yaw rate, and the angular velocities of the four wheels.…”

Section: Vehicle Modelmentioning

confidence: 99%

Application of Extended Kalman Filtering for Estimating Immeasurable Vehicle State Variables

Anh

2017

J. ADV. ENG. COMPUT.

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This paper presents an extended Kalman filtering (EKF) algorithm for estimating immeasurable state variables of a vehicle stability control system. Initially, the steering angle and vertical forces on the tires were considered inputs of the estimator. The measured process outputs were the sensor signals egarding longitudinal and lateral accelerations, steering angle, yaw rate, and wheel speed. Subsequently, by using Euler discretization for a seven-degree-of-freedom nonlinear vehicle model, difficult-to-measure state variables such as lateral velocity, vehicle side-slip angle, and lateral tire forces were identified separately by using the EKF algorithm. The estimation results of the proposed control system evidenced high performance. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Section: Vehicle Modelmentioning

confidence: 99%

Application of Extended Kalman Filtering for Estimating Immeasurable Vehicle State Variables

Anh

2017

J. ADV. ENG. COMPUT.

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“…To develop and test algorithms and control loop strategies a three dimensional model that includes wheel spins, suspension characteristics and tyre non-linearities was developed on the basis of the model described in [20]. Particular attention was dedicated to the centrifugal clutch modeling in order to simulate startstop maneuvers and investigated the effect of the clutch skid on the longitudinal dynamics.…”

Section: B Vehicle Dynamic Modelmentioning

confidence: 99%

Development of a reduced size unmanned car

Bertolazzi

Bosetti

Cecco

et al. 2008

2008 10th IEEE International Workshop on Advanced Motion Control

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This article describes the overall system design of a reduced size autonomous vehicle and focuses on the control strategy which is based on a Nonlinear Receding Horizon Control (NRHC) algorithm. The NRH planner is called by a high level manager, in the outer control loop, to solve a sequence of optimal control problems. The planned motion provide a sequence of reference set points for the faster inner control loop until a new plan is available. The lateral motion is controlled through the steering command by tracking the yaw rate reference. The longitudinal motion is controlled by means of the throttle/braking coupled command by tracking the planned forward speed profiles. The paper discusses the results of some simulation results. The project description proceeds as follows: Section II describes the system architecture from vehicle and communication to software. Section III and IV details the motion planning and control algorithms. In Section V the simulation results regarding the NRH based controller are discussed

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“…It also allows the accelerations calculated at every time step to be plotted in real time. This was the approach used in the NAVDyn Model (Demerly & Youcef-Toumi, 2000); all portions of the model existed as connected block diagrams. However, Simulink also supports the use of *.m files, and allows the combination of block diagrams and modular programming.…”

Section: Existing Vehicle Modelsmentioning

confidence: 99%

“…NAVDyn was written entirely in Simulink using block diagrams. The third data set arises from actual accelerometer measurements taken off a real Ford Taurus (Demerly & Youcef-Toumi, 2000).…”

Section: Dvm Validationmentioning

confidence: 99%

“…This shift changes the normal forces experiences by the tires (for example, the outside wheels of a vehicle in a turn will generate a larger normal force, while the inner wheels will have less force). To determine the effect of the DWTFs, a simulation was run with a step turn of 42° using the vehicle data from a Ford Taurus (Demerly & Youcef-Toumi, 2000). Based on the results seen in Figure 24- However, even for values of , the effect of the DWTFs do not contribute much to the tire normal forces Based on these results, the following assumption was made:…”

Section: Model Predictive Controlmentioning

confidence: 99%

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A Model Predictive Control Approach to Roll Stability of a Scaled Crash Avoidance Vehicle

Noxon¹

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In this paper, a roll stability controller (RSC) is presented based on an eight degree of freedom dynamic vehicle model. The controller is designed for and tested on a scaled vehicle performing obstacle avoidance maneuvers on a populated test track. A rapidly-exploring random tree (RRT) algorithm is used for the vehicle to execute a trajectory around an obstacle, and examines the geographic, non-homonymic, and dynamic constraints to maneuver around the obstacle. A model predictive controller (MPC) uses information about the vehicle state and, based on a weighted performance measure, generates an optimal trajectory around the obstacle. The RSC uses the standard vehicle state sensors: four wheel mounted encoders, a steering angle sensor, and a six degree of freedom inertial measurement unit (IMU). An emphasis is placed on the mitigation of rollover and spin-out, however if a safe maneuver is not found and a collision is inevitable, the program will run a brake command to reduce the vehicle speed before impact. The trajectory is updated at a rate of 20 Hz, providing improved stability and maneuverability for speeds up to 10 ft/s and turn angles of up to 20°.

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Non-Linear Analysis of Vehicle Dynamics (NAVDyn): A Reduced Order Model for Vehicle Handling Analysis

Cited by 21 publications

References 5 publications

Application of Extended Kalman Filtering for Estimating Immeasurable Vehicle State Variables

Application of Extended Kalman Filtering for Estimating Immeasurable Vehicle State Variables

Development of a reduced size unmanned car

A Model Predictive Control Approach to Roll Stability of a Scaled Crash Avoidance Vehicle

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