Wu, H., Zeng, X.H., Gao, D.G., et al.: Dynamic stability of an electromagnetic suspension maglev vehicle under steady aerodynamic load. Javadi, A., Alizadeh, G., Ghiasi, A.R., et al.: Robust control of electromagnetic levitation system. Yaseen, M.H.: A comparative study of stabilizing control of a planer electromagnetic levitation using PID and LQR controllers. ![]() Kong, E., Song, J.S., Kang, B.B., et al.: Dynamic response and robust control of coupled maglev vehicle and guideway system. The influence of speed control gain is studied numerically on the periodic and chaotic solutions, which the bifurcation parameter interval is found for the control gains. Second, the dynamic control equation of the negative stiffness system is studied for the maglev vehicle, and a bifurcation control method is obtained to change the system stiffness. The displacement, speed control parameters, and equivalent mass of a maglev vehicle are the important parameters that affect the amplitude and stability of the vibration system. The vibration characteristics and stability of the maglev vehicle are analyzed. First, the dynamic control equation of positive stiffness of the maglev vehicle is analyzed by the multiple-scale method, and the amplitude–frequency response curve of the maglev vehicle is obtained. It is found that the vibration proportional control gains can change the stiffness of the maglev control system, and three kinds of stiffness, such as positive, negative, and zero stiffness, are obtained for the maglev control systems. The nonlinear dynamic differential equation of the maglev vehicle was derived with proportional and differential current controllers. A single-degree-of-freedom model of the maglev vehicle is studied considering a periodic unsteady aerodynamic disturbance and the variation of carriage mass. Markov chain Monte Carlo identifiability moment dynamics noise stochastic differential equations stochasticity.The unsteady aerodynamic force on a maglev vehicle increases sharply with the increase in speed of the maglev vehicle, which can increase the difficulty of electromagnetic suspension stability control. All code used to perform the analysis is available on Github. Our analysis shows that SDE models can often extract more information about parameters than deterministic descriptions. Using practically motivated synthetic data and Markov chain Monte Carlo methods, we assess parameter identifiability in the context of available data. To assess structural identifiability, we study ODEs that describe the statistical moments of the stochastic process using open-source software tools. We provide an accessible introduction to identifiability analysis and demonstrate how existing ideas for analysis of ODE models can be applied to stochastic differential equation (SDE) models through four practical case studies. Identifiability analysis is well-established for deterministic, ordinary differential equation (ODE) models, but there are no commonly adopted methods for analysing identifiability in stochastic models. Such issues of parameter identifiability have important ramifications for both the predictive power of a model, and the mechanistic insight that can be obtained. Whether or not reliable parameter estimates are obtainable from the available data can easily be overlooked. Mathematical models are routinely calibrated to experimental data, with goals ranging from building predictive models to quantifying parameters that cannot be measured.
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