199 research outputs found
Mathematical control of complex systems
Copyright © 2013 ZidongWang et al.This is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
Capacity of Control for Stochastic Dynamical Systems Perturbed by Mixed Fractional Brownian Motion with Delay in Control
In this paper, we discuss the relationships between capacity of control in
entropy theory and intrinsic properties in control theory for a class of finite
dimensional stochastic dynamical systems described by a linear stochastic
differential equations driven by mixed fractional Brownian motion with delay in
control. Stochastic dynamical systems can be described as an information
channel between the space of control signals and the state space. We study this
control to state information capacity of this channel in continuous time. We
turned out that, the capacity of control depends on the time of final state in
dynamical systems. By using the analysis and representation of fractional
Gaussian process, the closed form of continuous optimal control law is derived.
The reached optimal control law maximizes the mutual information between
control signals and future state over a finite time horizon. The results
obtained here are motivated by control to state information capacity for linear
systems in both types deterministic and stochastic models that are widely used
to understand information flows in wireless network information theory.
The contribution of this paper is that we propose some new relationships
between control theory and entropy theoretic properties of stochastic dynamical
systems with delay in control. Finally, we present an example that serve to
illustrate the relationships between capacity of control and intrinsic
properties in control theory.Comment: 17 pages, 2 example
Almost Sure Asymptotical Adaptive Synchronization for Neutral-Type Neural Networks with Stochastic Perturbation and Markovian Switching
The problem of almost sure (a.s.) asymptotic adaptive synchronization for neutral-type neural networks with stochastic perturbation and Markovian switching is researched. Firstly, we proposed a new criterion of a.s. asymptotic stability for a general neutral-type stochastic differential equation which extends the existing results. Secondly, based upon this stability criterion, by making use of Lyapunov functional method and designing an adaptive controller, we obtained a condition of a.s. asymptotic adaptive synchronization for neutral-type neural networks with stochastic perturbation and Markovian switching. The synchronization condition is expressed as linear matrix inequality which can be easily solved by Matlab. Finally, we introduced a numerical example to illustrate the effectiveness of the method and result obtained in this paper
H
This paper addresses the problem of H∞ control for a class of uncertain stochastic systems with Markovian switching and time-varying delays. The system under consideration is subject to time-varying norm-bounded parameter uncertainties and an unknown nonlinear function in the state. An integral sliding surface corresponding to every mode is first constructed, and the given sliding mode controller concerning the transition rates of modes can deal with the effect of Markovian switching. The synthesized sliding mode control law ensures the reachability of the sliding surface for corresponding subsystems and the global stochastic stability of the sliding mode dynamics. A simulation example is presented to illustrate the proposed method
The 2nd International Conference on Mathematical Modelling in Applied Sciences, ICMMAS’19, Belgorod, Russia, August 20-24, 2019 : book of abstracts
The proposed Scientific Program of the conference is including plenary lectures, contributed oral talks, poster sessions and listeners. Five suggested special sessions / mini-symposium are also considered by the scientific committe
- …