Topic
Vehicle dynamics
About: Vehicle dynamics is a research topic. Over the lifetime, 12909 publications have been published within this topic receiving 204091 citations.
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TL;DR: In this article, a review indicates that the proper utilization of neural networks offers a feasible approach to help meet current and future technological needs in aeronautics, including the implementation of active control devices to harness or suppress unsteady aerodynamic effects such as dynamic stall on helicopter rotor blades.
74 citations
12 May 2009
TL;DR: A method for calculating visual odometry for ground vehicles with car-like kinematic motion constraints similar to Ackerman's steering model is presented and a simple and practical solution to the odometry calculation by clever placement of a single camera is shown.
Abstract: A method for calculating visual odometry for ground vehicles with car-like kinematic motion constraints similar to Ackerman's steering model is presented. By taking advantage of this non-holonomic driving constraint we show a simple and practical solution to the odometry calculation by clever placement of a single camera. The method has been implemented successfully on a large industrial forklift and a Toyota Prado SUV. Results from our industrial test site is presented demonstrating the applicability of this method as a replacement for wheel encoder-based odometry for these vehicles.
74 citations
TL;DR: A stochastic model predictive control (SMPC) approach is proposed to model the driver steering skill, which effectively incorporates the random variations in the road friction and roughness, a multipoint preview approach, and a piecewise affine model structure that are developed to mimic the driver's perception of the desired path and the nonlinear internal vehicle dynamics.
Abstract: Great advances in simulation-based vehicle system design and development of various driver assistance systems have enhanced the research on improved modeling of driver steering skills. However, little effort has been made on developing driver steering skill models while capturing the uncertainties or statistical properties of the vehicle-road system. In this paper, a stochastic model predictive control (SMPC) approach is proposed to model the driver steering skill, which effectively incorporates the random variations in the road friction and roughness, a multipoint preview approach, and a piecewise affine (PWA) model structure that are developed to mimic the driver's perception of the desired path and the nonlinear internal vehicle dynamics. The SMPC method is then used to generate a steering command by minimization of a cost function, including the lateral path error and ease of driver control. In the analyses, first, the experimental data of Hongqi HQ430 are used to validate the driver steering skill controller. Then, the parametric studies of control performance during a nonlinear steering maneuver are provided. Finally, further discussions about the driver's adaption and the indication on vehicle dynamics tuning are given. The proposed switching-based SMPC driver steering control framework offers a new approach for driver behavior modeling.
74 citations
TL;DR: The theoretical background of multibody dynamics is presented, the efficiency of recursive algorithms is shown, methods for dynamical analysis are summarized, and applications to vehicle dynamics and biomechanics are reported.
Abstract: Multibody system dynamics is an essential part of computational dynamics a topic more generally dealing with kinematics and dynamics of rigid and flexible systems, finite elements methods, and numerical methods for synthesis, optimization and control including nonlinear dynamics approaches. The theoretical background of multibody dynamics is presented, the efficiency of recursive algorithms is shown, methods for dynamical analysis are summarized, and applications to vehicle dynamics and biomechanics are reported. In particular, the wear of railway wheels of high-speed trains and the metabolical cost of human locomotion is analyzed using multibody system methods.
74 citations
TL;DR: The results demonstrate that compared with the baseline case, the improvements in the equivalent fuel and energy consumed in the two-variable optimization case were 30.75% and 59.55%, respectively, during the standard European city driving cycle.
74 citations