A study on body sway of car-trailer combinations considering dry friction in steering subsystem

Considering the stability of vehicle system, static instability and dynamic instability are two different instability problems. Because of the dynamic coupling between car and trailer, the problem of dynamic stability of car-trailer combination (CTC) is more obvious. This instability is called body sway or flutter in engineering, its boundary is often described by dynamic critical speed ( vcrit). It has been proved by experiments that the steering system characteristics have an important impact on the dynamic stability of CTC, but the specific mechanism is not clear. In this paper, the characteristic and influence of steering subsystem are studied for the first time. Firstly, a 6-DOF nonlinear dynamic model of CTC is established by Lagrange equation. The steering subsystem characteristics, incl. stiffness, damping, rotational inertia, and dry friction, are considered in theoretical modeling. On this basis, the influences of steering characteristics, especially the dry friction, on vcrit and axle cornering stiffness of CTC are analyzed. Simulation results show that the vcrit can increase by 16% and 23.2% respectively via adjusting the steering stiffness and the sliding friction factor. Therefore, a fine selection of steering subsystem characteristics can effectively improve the dynamic stability and safety of CTC. The research results of this paper can provide reference for the design of steering system considering dynamic stability.

Self-Steering Characteristics of FSAE Vehicle with a Linear Bicycle Model

A FSAE car must exhibit precise and predictable handling behaviour since it is subject to driving manoeuvres in dynamic conditions. Therefore, an accurate prediction of its self-steering characteristics becomes vitally important, especially in the expected lateral acceleration operating range. The simulation implements a linear bicycle model of FSAE car in MATLAB and establishes the understeer gradient and the critical speed, thereby aiding the analysis of the steering wheel angle variation required to negotiate the corners of increasing dynamics.

Application of Measurement Sensors and Navigation Devices in Experimental Research of the Computer System for the Control of an Unmanned Ship Model

Unmanned autonomous transport vessels (MASS) are the future of maritime transport. The most important task in the design and construction of unmanned ships is to develop algorithms and a computer program for autonomous control. In order for such a computer program to properly control the ship (realizing various functions), the ship must be equipped with a computer system as well as measurement sensors and navigation devices, from which the recorded parameters are processed and used for autonomous control of the ship. Within the framework of conducted research on autonomous ships, an experimental model of an unmanned ship was built. This model was equipped with a propulsion system not commonly used on transport vessels (two azimuth stern thrusters and two bow tunnel thrusters), but providing excellent propulsion and steering characteristics. A complete computer system with the necessary measuring sensors and navigation devices has also been installed in the model of the ship, which enables it to perform all functions during autonomous control. The objective of the current research was to design and build a prototype computer system with the necessary measurement sensors and navigation devices with which to autonomously control the unmanned ship model. The designed computer system is expected to be optimal for planned tasks during control software tests. Tests carried out on open waters confirmed the correctness of the operation of the computer system and the entire measurement and navigation equipment of the built model of the unmanned transport vessel.

Robust steering assistance control for tracking large-curvature path considering uncertainties of driver’s steering behavior

A human-machine shared steering control is presented in this paper for tracking large-curvature path, considering uncertainties of driver’s steering characteristics. A driver-vehicle-road (DVR) model is proposed in which uncertain characteristic parameters are defined to describe the human driver’s steering behaviors in tracking large-curvature path. Then the radial basis function neural network (RBF) is used to estimate parameters of different drivers’ characteristics and to obtain the boundaries of these parameters. Parameter uncertainties of the driver’s steering characteristics and time-varying vehicle speed of the DVR model are handled with the Takagi-Sugeno (T-S) fuzzy logic. And these parameter uncertainties are considered in the design of the shared steering controller. Then based on the DVR model, a T-S fuzzy full-order dynamic compensator with D-pole assignment is designed to assist driver’s steering for tracking path with large curvature. Simulation results show that the proposed controller can provide individual levels of steering assistance in path following according to driver’s proficiency, and can improve driving comfort significantly.

Path Planning for Autonomous Articulated Vehicle Based on Improved Goal-Directed Rapid-Exploring Random Tree

The special steering characteristics and task complexity of autonomous articulated vehicle (AAV) make it often require multiple forward and backward movements during autonomous driving. In this paper, we present a simple yet effective method, named head correction with fixed wheel position (HC-FWP), for the demand of multiple forward and backward movements. The goal-directed rapid-exploring random tree (GDRRT) algorithm is first used to search for a feasible path in the obstacle map, and then, the farthest node search (FNS) algorithm is applied to obtain a series of key nodes, on which HC-FWP is used to correct AAV heading angles. Simulation experiments with Dynapac CC6200 articulated road roller parameters show that the proposed improved goal-directed rapid-exploring random tree (IGDRRT), consisting of GDRRT, FNS, and HC-FWP, can search a feasible path on maps that require the AAV to move back and forth.