scholarly journals Quantitative and Qualitative Methods in the Study of some Dynamic Systems

2015 ◽  
Vol 13 ◽  
pp. 168-171 ◽  
Author(s):  
Dumitru Bălă

In this paper we present several methods for the study of stability of dynamical systems. We analyze the stability of a hammer modeled by the free vibrator that collides with a sprung elastic mass taking into consideration the viscous damping too.

2021 ◽  
Vol 3 ◽  
pp. 5-17
Author(s):  
Denis Khusainov ◽  
◽  
Alexey Bychkov ◽  
Andrey Sirenko ◽  
Jamshid Buranov ◽  
...  

This work is devoted to the further development of the study of the stability of dynamic systems with switchings. There are many different classes of dynamical systems described by switched equations. The authors of the work divide systems with switches into two classes. Namely, on systems with definite and indefinite switchings. In this paper, the system with certain switching, namely a system composed of differential and difference sub-systems with the condition of decreasing Lyapunov function. One of the most versatile methods of studying the stability of the zero equilibrium state is the second Lyapunov method, or the method of Lyapunov functions. When using it, a positive definite function is selected that satisfies certain properties on the solutions of the system. If a system of differential equations is considered, then the condition of non-positiveness (negative definiteness) of the total derivative due to the system is imposed. If a difference system of equations is considered, then the first difference is considered by virtue of the system. For more general dynamical systems (in particular, for systems with switchings), the condition is imposed that the Lyapunov function does not increase (decrease) along the solutions of the system. Since the paper considers a system consisting of differential and difference subsystems, the condition of non-increase (decrease of the Lyapunov function) is used.For a specific type of subsystems (linear), the conditions for not increasing (decreasing) are specified. The basic idea of using the second Lyapunov method for systems of this type is to construct a sequence of Lyapunov functions, in which the level surfaces of the next Lyapunov function at the switching points are either «stitched» or «contain the level surface of the previous function».


1987 ◽  
Vol 109 (4) ◽  
pp. 410-413 ◽  
Author(s):  
Norio Miyagi ◽  
Hayao Miyagi

This note applies the direct method of Lyapunov to stability analysis of a dynamical system with multiple nonlinearities. The essential feature of the Lyapunov function used in this note is a non-Lure´ type Lyapunov function which surpasses the Lure´-type Lyapunov function from the point of view of the stability region guaranteed. A modified version of the multivariable Popov criterion is used to construct non-Lure´ type Lyapunov function, which allow for the dynamical sytems with multiple nonlinearities.


2015 ◽  
Vol 17 (8) ◽  
pp. 083025 ◽  
Author(s):  
Paul Kirk ◽  
Delphine M Y Rolando ◽  
Adam L MacLean ◽  
Michael P H Stumpf

2021 ◽  
Author(s):  
Jingmeng Cui ◽  
Merlijn Olthof ◽  
Anna Lichtwarck-Aschoff ◽  
Tiejun Li ◽  
Fred Hasselman

We present the simlandr package for R, which provides a set of tools for constructing potential landscapes for dynamic systems using Monte Carlo simulation. Potential landscapes can be used to quantify the stability of system states. While the canonical form of a potential function is defined for gradient systems, generalized potential functions can also be defined for non-gradient dynamical systems. Our method is based on the potential landscape definition by Wang, Xu, and Wang (2008), and can be used for a large variety of models. Using two multistable dynamical systems as examples, we illustrate how simlandr can be used for model simulation, landscape construction, and barrier height calculation.


2021 ◽  
Author(s):  
Blanka Balogh ◽  
David Saint-Martin ◽  
Aurélien Ribes

<p>The development of atmospheric parameterizations based on neural networks is often hampered by numerical instability issues. Previous attempts to replicate these issues in a toy model have proven ineffective. We introduce a new toy model for atmospheric dynamics, which consists in an extension of the Lorenz'63 model to a higher dimension. While neural networks trained on a single orbit can easily reproduce the dynamics of the Lorenz'63 model, they fail to reproduce the dynamics of the new toy model, leading to unstable trajectories. Instabilities become more frequent as the dimension of the new model increases, but are found to occur even in very low dimension. Training the neural network on a different learning sample, based on Latin Hypercube Sampling, solves the instability issue. Our results suggest that the design of the learning sample can significantly influence the stability of dynamical systems driven by neural networks.</p>


2019 ◽  
Vol 34 ◽  
pp. 123-128
Author(s):  
Dumitru Bălă

The paper includes the stability study of some dynamical systems given by systems of differential equations. The paper examines the stability of three dynamic systems using the Leapunov function method. The originality of the paper consists of how we choose the Leapunov function. We apply the stability theorems given by Leapunov for autonomous systems. Stability is an important property of a dynamic system that has applications in the technique.


Sign in / Sign up

Export Citation Format

Share Document