A differential inclusion approach for modeling and analysis of dynamical systems under uncertainty

2012 ◽  
Vol 17 (2) ◽  
pp. 239-253 ◽  
Author(s):  
Jorge Barrios ◽  
Alain Piétrus ◽  
Gonzalo Joya ◽  
Aymée Marrero ◽  
Héctor de Arazoza
Author(s):  
Diego Colón ◽  
Átila Madureira Bueno ◽  
Ivando S. Diniz ◽  
Jose M. Balthazar

The Ball and Beam system is a common didactical plant that presents a complex nonlinear dynamics. This comes from the fact that the ball rolls over the beam, which rotates around its barycenter. In order to deduce the system’s equations, composition of movement must be applied, using a non-inertial reference frame attached to the beam. In the Literature, a common hypothesis is to suppose that the ball rolls without slipping. If a viscous friction is supposed to be present, a simpler situation is obtained, where Lagrangean mechanics can be applied, and no contact force is known. Even then, the dynamics is very nonlinear. However, this model does not include all the relevant phenomena, such as ball’s slipping at higher beam’s inclination angles, dry friction between the ball and the beam, and impacts between: 1) the ball and the ends of the beam, and 2) the beam and the base (ground). These additions to the model impose the necessity to calculate, in a simulation setting, the contact forces, and the Newton’s approach to determine the system’s equations becomes more convenient. Also, discontinuities in the model are introduced, and the simpler mathematical object for model such systems are the differential inclusion systems. In this work, we deduce the Ball and Beam differential inclusion system, including dry friction and the impact between the ball and beam. We also present simulation results for the corresponding differential inclusion system in a typical situation.


2012 ◽  
Vol 26 (25) ◽  
pp. 1246016
Author(s):  
ZDENĚK BERAN ◽  
SERGEJ ČELIKOVSÝ

This contribution addresses a possible description of the chaotic behavior in multivalued dynamical systems. For the multivalued system formulated via differential inclusion the practical conditions on the right-hand side are derived to guarantee existence of a solution, which leads to the chaotic behavior. Our approach uses the notion of the generalized semiflow but it does not require construction of a selector on the set of solutions. Several applications are provided by concrete examples of multivalued dynamical systems including the one having a clear physical motivation.


2021 ◽  
Vol 25 (2(36)) ◽  
pp. 83-94
Author(s):  
D. G. Kartashov ◽  
M. S. Tairova

The article proposes two algorithms for the numerical construction of the convex hull of a set in three-dimensional space using its support function. The first uses the hyperplane intersection method to find the pivot points of a set. The second one is based on the deformation function and allows you to find an arbitrary point of the convex hull of a set, which is convenient in many applications. The algorithms are compared, and asymptotic complexities are found. The application of the proposed apparatus to finding the destination set of dynamical systems is shown. The dynamic system will be based on differential inclusion.


Entropy ◽  
2021 ◽  
Vol 23 (5) ◽  
pp. 583
Author(s):  
Pavel Kraikivski

Random fluctuations in neuronal processes may contribute to variability in perception and increase the information capacity of neuronal networks. Various sources of random processes have been characterized in the nervous system on different levels. However, in the context of neural correlates of consciousness, the robustness of mechanisms of conscious perception against inherent noise in neural dynamical systems is poorly understood. In this paper, a stochastic model is developed to study the implications of noise on dynamical systems that mimic neural correlates of consciousness. We computed power spectral densities and spectral entropy values for dynamical systems that contain a number of mutually connected processes. Interestingly, we found that spectral entropy decreases linearly as the number of processes within the system doubles. Further, power spectral density frequencies shift to higher values as system size increases, revealing an increasing impact of negative feedback loops and regulations on the dynamics of larger systems. Overall, our stochastic modeling and analysis results reveal that large dynamical systems of mutually connected and negatively regulated processes are more robust against inherent noise than small systems.


2016 ◽  
Vol 2 (1) ◽  
Author(s):  
Alexander Garza ◽  
◽  
Megan Eberle ◽  
Eric A. Eager ◽  
◽  
...  

2001 ◽  
Vol 01 (01) ◽  
pp. 63-83 ◽  
Author(s):  
KLAUS REINER SCHENK-HOPPÉ

This paper surveys recent advances in the application of random dynamical systems theory in economics. It illustrates the usefulness of this framework for modeling and analysis of economic phenomena with stochastic components, mainly focusing on stochastic dynamic models of economic growth. The paper also highlights some directions for further applications and interdisciplinary research on random dynamical systems.


2009 ◽  
Vol 2009 ◽  
pp. 1-9
Author(s):  
Nihal Ege ◽  
Khalik G. Guseinov

The boundedness of the motions of the dynamical system described by a differential inclusion with control vector is studied. It is assumed that the right-hand side of the differential inclusion is upper semicontinuous. Using positionally weakly invariant sets, sufficient conditions for boundedness of the motions of a dynamical system are given. These conditions have infinitesimal form and are expressed by the Hamiltonian of the dynamical system.


Symmetry ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 2326
Author(s):  
Alexander J. Zaslavski

In this paper, we study the turnpike phenomenon for trajectories of continuous-time dynamical systems generated by differential inclusions, which have a prototype in mathematical economics. In particular, we show that, if the differential inclusion has a certain symmetric property, the turnpike possesses the corresponding symmetric property. If we know a finite number of approximate trajectories of our system, then we know the turnpike and this information can be useful if we need to find new trajectories of our system or their approximations.


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