Construction Of Lyapunov Functions Research Articles

Age Structured Deterministic Model of Diphtheria Infection

Age-structured mathematical model of diphtheria infection has been formulated with specific epidemiological classes such as S1, susceptible infant at time t (0-1years), S2, susceptible school children population at time t, V, vaccination population at time t, E, exposed population at time t, I1, asymptomatic infection population at time t, I2, symptomatic infection population at time t, ID, detected infectious humans at time t (asymptomatic and symptomatic) population through testing, R, recovered population at time t. It was established through theorems and proofs that the model is epidemiologically meaningful, and that all its state variables are positive (non-negative) at time t>0 in the domain ℘, and that the domain ℘ is indeed bounded. Using the next generation matrix, the reproduction ratio Rb of the system was determined. Using dynamical system theory, it was established that the system is locally stable. A matrix-theoretic method was used in the construction of an appropriate Lyapunov function for the global stability analysis of the formulated model, and also established that the system is globally asymptotically stable if Rb≤1 and unstable otherwise.

Local-in-space wave breaking criteria for a shallow water wave equation

A shallow water wave equation including the famous Degasperis-Procesi model is considered. Firstly, the L 2 conservation law for the equation is derived. Secondly, using the methods of transport equation, we establish the boundedness of solutions for the shallow water wave equation. Finally, utilizing the approaches to construct Lyapunov functions, we find local-in-space wave breaking criteria of its solutions on the line R and circle S under certain restrictions on the coefficients of the equation.

Dynamics analysis, synchronization and FPGA implementation of multiscroll Hopfield neural networks with non-polynomial memristor

The number of attractors in a memristor-based multiscroll Hopfield Neural Network (HNN) is typically coupled with the number of polynomials, which leads to a coupling between the computational complexity and resource utilization in circuit implementation. To decouple this relationship, we propose a non-polynomial memristor that satisfies the Lipschitz condition. Regardless of whether it is used to simulate synaptic behavior, simulate the impact of electromagnetic radiation, or a combination of both scenarios, it can conveniently control the generation of single-direction or multiple-direction multiscroll attractors without adding or reducing any terms. By constructing Lyapunov functions, the sufficient condition for these multiscroll memristor HNNs to be bounded is obtained. After improving the feasibility of linear matrix inequalities, a strongly adaptive observer is proposed. After uniting an adaptive sliding mode control method, we propose a new adaptive synchronization scheme to simulate neural network synchronization. Finally, the digital circuit implementation and functional verification of these memristor-based multiscroll HNNs are completed using a field-programmable gate array (FPGA). Based on this, an image encryption circuit is designed so that the FPGA can directly encrypt images and transmit them to the IO device.

Synchronization of inertial complex-valued memristor-based neural networks with time-varying delays.

The synchronization of inertial complex-valued memristor-based neural networks (ICVMNNs) with time-varying delays was explored in the paper with the non-separation and non-reduced approach. Sufficient conditions required for the exponential synchronization of the ICVMNNs were identified with the construction of comprehensive Lyapunov functions and the design of a novel control scheme. The adaptive synchronization was also investigated based on the derived results, which is easier to implement in practice. What's more, a numerical example that verifies the obtained results was presented.

Global dynamics of a delayed model with cytokine-enhanced viral infection and cell-to-cell transmission

<abstract><p>Recent studies have demonstrated the superiority of cell-to-cell transmission over cell-free virus infection, and highlighted the role of inflammatory cytokines in enhancing viral infection. To investigate their impacts on viral infection dynamics, we have proposed an HIV infection model incorporating general incidence rates, these infection modes, and two time delays. We derived the basic reproduction number and showed that it governs the existence and local stability of steady states. Through the construction of appropriate Lyapunov functionals and application of the LaSalle invariance principle, we established the global asymptotic stability of both the infection-free and infected steady states.</p></abstract>

CONSTRUCTION OF A SET OF DIFFERENTIAL EQUATIONS SYSTEMSAND STABILITY IN THE VICINITY OF A PROGRAM MANIFOLD

The problem of constructing a entire set of differential equation systems for a given program manifold is addressed. Necessary and sufficient conditions have been compiled to ensure that the program manifold is integral to the systems being developed. These conditions form a rectangular linear algebraic system in relation to the required functions. Using the property of a rectangular matrix, the set of differential equation systems was constructed. Additionally, the problem of designing indirect automatic control systems with rigid feedback is explored. Since the given program is not always executed perfectly due to initial or ongoing disturbances, it is reasonable to require stability of the program manifold with respect to a certain function. This leads to the analysis of the stability of the system of equations in relation to the given program manifold. Through the construction of Lyapunov functions for the system in canonical form, sufficient conditions for the absolute stability of the program manifold are derived. These results can be applied to the design of stable automatic indirect control systems.

Global dynamics of a diffusive SARS-CoV-2 model with antiviral treatment and fractional Laplacian operator

In this paper, we propose and investigate the global dynamics of a SARS-CoV-2 infection model with diffusion and antiviral treatment. The proposed model takes into account the two modes of transmission (virus-to-cell and cell-to-cell), the lytic and nonlytic immune responses. The diffusion into the model is formulated by the regional fractional Laplacian operator. Furthermore, the global asymptotic stability of equilibria is rigorously established by means of a new proposed method constructing Lyapunov functions for a class of partial differential equations (PDEs) with regional fractional Laplacian operator. The proposed method is applied to the classical reaction-diffusion equations with normal diffusion.

Dynamical Analysis and Misalignment Projection Synchronization of a Novel RLCM Fractional-Order Memristor Circuit System

In this paper, a simple and novel fractional-order memristor circuit is established, which contains only resistance, inductance, capacitance and memristor. By using fractional calculus theory and the Adomian numerical algorithm, special bifurcations, chaotic degradation, C0 and Spectral Entropy (SE) complexity under one-dimensional and two-dimensional parameter variations with different orders, parameters and initial memristor values of the system were studied. Meanwhile, in order to better utilize the applications of fractional-order memristor systems in communication and security, a misalignment projection synchronization scheme for fractional-order systems is proposed, which overcomes the shortcomings of constructing Lyapunov functions for fractional-order systems to prove stability and designing controllers for the Laplace transform matrix.

Quasi-invariant and attracting sets of competitive neural networks with time-varying and infinite distributed delays

This paper focuses on the quasi-invariant set (QIS), global attracting set (GAS) and global exponential attracting set (GEAS) of competitive neural networks (CNNs) with time-varying and infinite distributed delays. For these purposes, based on the characteristics of nonnegative matrix and M-matrix, a new bidirectional delay integral inequality and a novel integro-differential inequality are first established. From the founded integral inequality, the existence conditions of the QIS and the GAS of the discussed system are obtained. Besides, the existence conditions of the GEAS are also given by the proposed integro-differential inequality, which gets rid of the construction of complex Lyapunov functions and functionals. The frameworks of the QIS, GAS and GEAS are also given. A numerical example is analyzed to confirm the validity of the obtained results in the end.

Dynamics of a Reaction-Diffusion System with Quiescence

In this paper, the dynamical behavior of a reaction-diffusion system with quiescence in a closed environment is investigated. The global existence of the solution is obtained by the upper and lower solution method, and the dissipative structure of the system is derived by constructing Lyapunov functions.

Fault estimation for cyber–physical systems with intermittent measurement transmissions via a hybrid observer approach

This paper investigates the problem of fault estimation for cyber–physical systems (CPSs) with intermittent measurement transmissions. A novel hybrid observer-based fault estimation scheme is proposed to generate estimations of the system state and the fault. Specifically, the correction terms of the designed observer are updated while new output measurements are available. Moreover, the estimation performance of the designed observer is analyzed within a framework of hybrid systems, and sufficient conditions are established to guarantee exponential input-to-state stability with a prescribed convergence rate based on the elaborately constructed Lyapunov function candidate. In contrast to the existing estimation schemes, the requirements of continuous or periodical communication are eliminated, and therefore the proposed one is more applicable for the modern industrial systems integrated with data communication networks. Finally, the application to an aircraft model is presented to illustrate the effectiveness of the developed methods.

Lyapunov function for interacting reinforced stochastic processes via Hopfield’s energy function

In 1984, Hopfield introduced an artificial neural network to understand the memory in living organisms. Under a condition of symmetry, he showed that there exists a Lyapunov function (known as energy function) for the network. In 2015, Budhiraja, Dupuis and Fischer introduced an idea to construct Lyapunov functions for nonlinear Markov processes via relative entropy. In this article, we introduce an approach based on the energy function of Hopfield networks to obtain Lyapunov functions for a class of interacting reinforced stochastic processes. Our result is an alternative to Budhiraja, Dupuis and Fischer’s approach and it works for the case of processes with finitely many 2-dimensional probability measures. We also bring from the neural network framework results about the total stability of differential equations. Finally, we include an application of the results to the study of differential equations associated to n≥2 vertex reinforced random walks on two-vertex graphs.

Passivity and underactuated modeling-based load energy coupling control for three-dimensional overhead crane systems

The conventional control strategies and methods with the three-dimensional model of overhead crane system are excessively dependent on model parameters and lead to the controllers design complex on position and anti-swing of overhead crane systems. With analysis on the coupling relationship among various state variables of overhead crane, the three-dimensional dynamic model of overhead crane based on mechanical virtual displacement and Lagrangian equation is constructed. A novel control strategy based on load energy coupling is designed to realize system positioning and eliminate the problem of residual load swing. The passivity of the three-dimensional overhead crane system is judged, and the energy composition of the system is deduced. The energy coupling function based on load displacement and swing angle is built. The control strategy of three-dimensional overhead crane system based on load energy coupling is designed by the Lyapunov stability theory and function construction method. The stability of the system is verified by the Lasalle invariance principle. With the experimental and programming, the proposed method is compared with different control methods. The results show that this method can realize the accurate positioning of the trolley more quickly and restrain the load swing more effectively.

Global stability and Turing instability deduced by cross-diffusion in a delayed diffusive cooperative species model

In this paper, we investigate the dynamical behavior of a delayed diffusive cooperative species model with cross-diffusion. Firstly, in the case of self-diffusion, we study the persistence properties and global stability of positive equilibrium for this model by constructing Lyapunov function. The obtained results reveal that the delay has no effect on the stability of positive equilibrium for this model. However, cross-diffusion can affect the stability of positive equilibrium of this model. Then, we continue to discuss Turing bifurcation on one-dimensional space and Turing pattern on two-dimensional space, which are deduced by cross-diffusion. For Turing bifurcation, choosing some cross-diffusion rate as bifurcation parameter, this model undergoes Turing bifurcation nearby the positive equilibrium as cross-diffusion rate is across the Turing bifurcation curve, and bifurcates the stable spatially inhomogeneous steady state solutions. For Turing pattern, we find out the Turing region under some conditions successfully. Selecting the different values of two cross-diffusion rates in the Turing region respectively, we carry out some numerical simulations and obtain spots pattern, spots-strip pattern and strip pattern.

A Lyapunov Theory for Finite-Sample Guarantees of Markovian Stochastic Approximation

The stochastic approximation (SA) method stands as the foundational mathematical tool for modern large-scale optimization and machine learning. Therefore, gaining a theoretical understanding of SA algorithms is of fundamental interest. In their paper titled “A Lyapunov Theory for Finite-Sample Guarantees of Markovian Stochastic Approximation,” Chen et al. present a unified Lyapunov framework for the finite-sample analysis of a Markovian SA algorithm under a contractive operator with respect to an arbitrary norm. The key novelty lies in the construction of a smooth Lyapunov function called the generalized Moreau envelope. The authors demonstrate the effectiveness of their SA results in the context of reinforcement learning (RL), specifically through popular algorithms such as variants of temporal difference (TD) learning and Q-learning. As byproducts, the results provide theoretical insights into the efficiency of bootstrapping in TD learning with eligibility traces and the bias-variance tradeoff in off-policy learning.

Stability and passivity analysis of higher‐order differential systems inspired by RLC circuits

SummaryThis paper discusses the global asymptotic stability and strong passivity analysis of fourth‐order nonlinear and time‐varying dynamical systems by utilizing the Lyapunov direct method. The mathematical model of the main system is obtained from a non‐linear and aging RLC circuit that we have designed before. RLC circuits play an excellent role in the stability of modern system theory. Without the concept of storage elements, the construction of Lyapunov or energy functions for nonlinear and time‐varying systems may be difficult. Because of this, although there are many studies on the stability concept, but the subject has not been completed yet. Therefore, this study may present some mathematical technicalities to the Lyapunov stability with physical considerations. The Lyapunov functions obtained from RLC circuits are natural storage functions, and they satisfy the dissipation inequality. The theoretical stability results of the system are also discussed by Lyapunov's linearization method. The relationship between stability and passivity is also given. Meanwhile, we realized that linear system analysis is not a guaranteed way for determining the stability properties of a full system. Finally, the correctness and availability of the proposed approach are verified through simulation results.

Synchronously discrete-time feedback control of large-scale systems

This paper studies the synchronously discrete-time feedback control of large-scale systems. Given an unstable complex networks (the ith subsystem is ẋi(t)=Aixi(t)+∑j=1NaijΓxj(t) ), we will design a discrete time feedback control Biei(tττ) to stabilize it. These discrete times are 0,τ,2τ,…, where τ>0 is the duration between two consecutive observations. When τ is sufficiently small, these discrete times are all over 0,∞. Thus we design an auxiliary system which is exponentially stable (the ith subsystem is eˆ̇i(t)=Aieˆi(t)+Bieˆi(t)+∑j=1NaijΓeˆj(t)), and hope to find a range of τ by the error of two feedback control systems. By constructing Lyapunov functions and using a graph-theoretic technique, we obtain a synchronization criterion.

New general decay results for a fully dynamic piezoelectric beam system with fractional delays under memory boundary controls

In this paper, we investigate the general stability results of a fully dynamic piezoelectric beam system with internal fractional delays under memory boundary controls. Firstly, by introducing two new equations and using two Volterra's inverse operators to deal with fractional delay terms and boundary memory terms, respectively, a new equivalent system is obtained. Secondly, by combining Faedo–Galerkin approximations with the compactness argument, we prove the local existence and uniqueness of weak solution. Finally, under a broader class of kernel functions, by constructing Lyapunov functionals and new equations and applying some technical lemmas, we establish the optimal and explicit energy decay results for two cases, namely, initial values and without imposing initial values equal to 0, that is, . It is worth pointing out that we focus more on the analysis of , because the stability results of are special cases of . This is the first time to analyze the stability of strong coupling problem by combining internal fractional delay and boundary viscoelastic damping. The obtained general decay results are not necessarily exponential or polynomial, which generalize the relevant results in the existing literature.

Global stability of a SEIR discrete delay differential‐difference system with protection phase

We consider an epidemiological model with the four classical compartments of susceptible, exposed, infected, and recovered population. We add a new compartment that is supposed to describe, for a limited time, individuals that are protected from the epidemic through vaccination or medication, for instance. We model the protection phase by an age‐structured partial differential equation. The age is the time since an individual entered the protection phase. The model is then reduced by integration on the characteristics to a differential‐difference system with delay. The discrete delay represents the limited duration of the protection phase. After establishing the basic properties of the model, we show that the disease‐free equilibrium (DFE) is globally asymptotically stable when the basic reproduction number is less than one and is unstable when this number is greater than one. Furthermore, we show that even if there is no mortality during the protection phase and the basic reproduction number is greater than one, the endemic equilibrium is globally asymptotically stable. The proofs of the global asymptotic stability of both equilibria are based on carefully constructed Lyapunov functions. To complete this study on the global dynamics, we discuss some results on weak and strong uniform persistence of the disease. Finally, numerical simulations are performed to illustrate and complete our main results.

Generalized Function Projective Synchronization of Two Different Chaotic Systems with Uncertain Parameters

This study proposes a new approach to realize generalized function projective synchronization (GFPS) between two different chaotic systems with uncertain parameters. The GFPS condition is derived by converting the differential equations describing the synchronization error systems into a series of Volterra integral equations on the basis of the Laplace transform method and convolution theorem, which are solved by applying the successive approximation method in the theory of integral equations. Compared with the results obtained by constructing Lyapunov functions or calculating the conditional Lyapunov exponents, the uncertain parameters and the scaling function factors considered in this paper have fewer restrictions, and the parameter update laws designed for the estimation of the uncertain parameters are simpler and easier to realize physically.