Showing 3 results for Bifurcation
D. Younesian, A.a. Jafari, R. Serajian,
Volume 1, Issue 3 (5-2011)
Abstract
Nonlinear hunting speeds of railway vehicles running on a tangent track are analytically obtained using Hopf bifurcation theory in this paper. The railway vehicle model consists of nonlinear primary yaw dampers, nonlinear flange contact stiffness as well as the clearance between the wheel flange and rail tread. Linear and nonlinear critical speeds are obtained using Bogoliubov method. A comprehensive parametric study is then carried out and effects of different parameters like the magnitudes of lateral clearance, damping values, wheel radius, bogie mass, lateral stiffness and the track gauge on linear and nonlinear hunting speeds are investigated.
M.h. Shojaeefard, S. Ebrahimi Nejad, M. Masjedi,
Volume 6, Issue 1 (3-2016)
Abstract
In this article, vehicle cornering stability and brake stabilization via bifurcation analysis has been investigated. In order to extract the governing equations of motion, a nonlinear four-wheeled vehicle model with two degrees of freedom has been developed. Using the continuation software package MatCont a stability analysis based on phase plane analysis and bifurcation of equilibrium is performed and an optimal controller has been proposed. Finally, simulation has been done in Matlab-Simulink software considering a sine with dwell steering angle input, and the effectiveness of the proposed controller on the aforementioned model has been validated with Carsim model.
Yavar Nourollahi Golouje, Seyyed Mahdi Abtahi, Majid Majidi,
Volume 12, Issue 2 (6-2022)
Abstract
In this paper, analysis and control of the chaotic vibrations in bounce dynamic of vehicle have been studied according to the comparison of controller based on the nonlinear control and chaos controller on the basis of the chaotic system properties. After modeling the vehicle dynamic, the chaotic behavior of the uncontrolled system was determined using combination of the numerical analysis including bifurcation diagrams and max Lyapunov exponent. The system parameters values were then identified in the quasi-periodic and chaotic behavior system. In order to eliminate the chaotic vibrations, the control signal was first developed using a nonlinear fast-terminal sliding mode control algorithm that its control gains are estimated online by fuzzy logic which was designed for vehicle vertical dynamics. Then the delayed feedback control was designed based on the development of Pyragas algorithm to control the system based on the properties of the chaotic system and generation of a small control signal. Comparison of the feedback system depicts priority of the Fuzzy-Pyragas controller in less energy consumption and better behavior.
