Vehicle dynamics

About: Vehicle dynamics is a research topic. Over the lifetime, 12909 publications have been published within this topic receiving 204091 citations.

Practical point stabilization of a nonholonomic mobile robot using neural networks

Abstract: A control structure that makes possible the integration of a kinematic controller and a neural network (NN) computed-torque controller for nonholonomic mobile robots is presented. This control algorithm is applied to the practical point stabilization problem i.e. stabilization to a small neighborhood of the origin. The NN controller proposed in this work can deal with unmodelled bounded disturbances and/or unstructured unmodelled dynamics in the vehicle. Online NN weight tuning algorithms that do not require off-line learning yet guarantee small tracking errors and bounded control signals are utilized.

A Radar Guided Vision System for Vehicle Validation and Vehicle Motion Characterization

Abstract: This paper describes a radar-guided monocular vision system that detects, validates, and tracks the preceding vehicle and thus predicts its lane-change intentions. A vision-based lane tracking process is developed to create a stable motion model in order to map the radar targets to image coordinates and consequently generate the region of interest (ROI) to search for a potential preceding vehicle. Model-based object classification algorithms are then applied to validate the existence of a vehicle in this ROI. Once the detected primary target vehicle, which is in the same lane as the host vehicle, is validated, it will be continuously tracked until it leaves the host lane. The spatial-temporal tracking history of the primary target vehicle is used to infer its intention of changing lanes. This prediction results from a characterization process for the primary target motion vector. The radar-vision integrated system has been evaluated on real-world data collected using a test vehicle equipped with a radar sensor, vision sensor, and a host processor.

A Dynamic Logistic Dispatching System With Set-Based Particle Swarm Optimization

Abstract: With the rapid development of e-commerce, logistics industry becomes a crucial component in the e-commercial ecological chain. Impelled by both economical and environmental benefit, logistics companies demand automated tools more urgently than ever. In this paper, a dynamic logistic dispatching system is proposed. The underlying model of the dispatching system is the dynamic vehicle routing problem which allows new orders being received as the working day progress. With this feature, the system becomes more practical than the systems with traditional static vehicle routing models, but is also more challenging as the vehicles must be scheduled in a dynamic way. The core of the system is a specially designed set-based particle swarm optimization algorithm. According to the characteristic of the problem, a new encoding scheme is defined by set and possibility, and a local refinement method is designed to accelerate the convergence speed of the algorithm. In addition, two more techniques: 1) region partition and 2) archive strategy are incorporated in the dispatching system to reduce the complexity of the problem and to facilitate the optimization process, helping the dispatcher control the vehicles in real time. The proposed system is tested on various benchmarks with different scales. Experimental results show that the proposed dispatching system is effective.

Vehicle Traction Control: Variable-Structure Control Approach

Abstract: A longitudinal one-wheel vehicle model is described for both anti-lock braking and anti-span acceleration. Based on this vehicle model sufficient conditions for applying sliding-mode control to a vehicle traction are derived via Lyapunov Stability Theory. With the understanding of these sufficient conditions, control laws are designed to control vehicle traction. Both the sufficient conditions and the control laws are verified using computer simulations.