A simple example in fluid dynamics is presented to show the important role of the law of increase of entropy in the dynamics. In a stationary 3-D viscous flow with negligible thermal conduction, the Lyapunov method is employed to obtain the fluid velocity "without" solving the equation of motion. This flow which is given around the point of maximum entropy consists of some closed orbits lying simultaneously on both surfaces of constant entropy and spherical surfaces centered at that point. Also the flow near to this stable point must be incompressible and non-viscous.

This paper studies the consensus problem of nonlinear leader-following multi-agent systems (MAS). To do this, the error dynamics between the leader agent and follower ones are described via a Takagi-Sugeno (TS) fuzzy model. If the obtained TS fuzzy model is stable, then all of the nonlinear agents reach consensus. The consensus problem is investigated based on the parameterized or fuzzy Lyapunov function combined with a technique of introducing slack matrices. The slack matrices cause to decouple the Lyapunov matrices from systems ones and therefore, sufficient consensus conditions are obtained in terms of linear matrix inequalities (LMIs). The proposed slack matrices add an extra degree-of-freedom to the LMI conditions and also decrease the conservativeness of the LMI-based conditions. Finally, in order to illustrate the effectiveness and merits of the proposed method, a numerical example for the consensus problem of nonlinear leader-follower MAS with thirteen followers is solved.

In this paper, estimation of second order polynomial systems’ domain of attraction (DA) via rational Lyapunov function is investigated. One of the methods for estimating DA is to find the greatest level set. In this study, in order to obtain the greatest level set, cost function based on increasing the region enclosed to the level set has been offered instead of using shape factor.Estimating DA has been converted into solving bilinear matrix inequality optimization problem.Capacity of this method compared to other methods in recent studies has been shown through some examples.

This paper presents a new control method based on Lyapunov function approach for simultaneous control of grid currents, circulating currents and modules voltages of a modular multilevel converter. Unlike existing methods that use nested and multi-loop control structures, the proposed method has a single-loop structure and ensures the global asymptotic stability of the closed-loop system. In this regard, first, the dynamic equations of the converter and the grid are extracted using the averaging technique, and then the coordinates of the steady-state operating point are calculated. Next, using the obtained operating point coordinates and the dynamic equations of the main system, the error dynamics are computed. Finally, these equations and the Lyapunov function, based on the error signals, are used for the analytic calculation of the control inputs. The simulation results confirm the efficiency of the proposed control method.

This paper introduces a robust hybrid adaptive control framework for stabilizing chaotic systems under persistent, potentially large time delays. The controller is based on an enhanced Lyapunov–Krasovskii functional that integrates an energy-capturing integral term with a bounded trigonometric term. The integral term accounts for historical effects by quantifying cumulative energy over the delay period, while the trigonometric term attenuates nonlinear oscillations. Embedding these components in a single control law yields stabilization of all state variables to the equilibrium despite substantial delays. We establish Uniform Ultimate Boundedness, showing that trajectories enter a compact neighborhood of the equilibrium after a finite transient and subsequently converge. Adjustable gains enable practitioners to determine the convergence radius and the size of the attraction region according to practical requirements. The method is validated on the delayed Lorenz system; simulations with a 20-second delay demonstrate rapid convergence to a small neighborhood of the equilibrium, with the Lyapunov functional derivative remaining non-positive. A comparative study with established controllers underscores the proposed approach’s favorable trade-offs among computational cost, oscillation suppression, and explicit stability guarantees. Overall, the proposed framework delivers a practical, robust, and high-performance solution for controlling chaotic systems in the presence of large time delays.

The dynamic Systems behaviours are investigating using the fourier tansform of data, the puankaveh surfaces, Iyapunov exponents calculations and attractor diemsion Study in phazzy space.At the among of mentioned methods, the Iyapunov exponents calculations are suitable method Contrary to the Iyapunov exponents Calculations for the existing equation of the behavioures of the systems, the using of the mentioned method for the laboratory investigations is very difficult. In this work, the Iynpunov exponent calculation estimated using Eckmonn method.

Lyapunov method is an effective method to prove the stability of the equilibrium points of dynamical systems. One of the applications of the Lyapunov analysis, is estimation of the region of attraction. In general, based on the selected Lyapunov function, a portion of the region of attraction is estimated that may not be the best. In this paper, via proposing an algorithm for generating Lyapunov candidate functions in a series from, a method is developed by which, in each step ahead, a modified estimation of the region of attraction is presented. The main idea of generation of the mentioned entries, is constructing a new Lyapunov function using a linear combination of the current Lyapunov function with its derivative. According to each candidate Lyapunov function, a region of attraction is constructed so that in the generation process of the new functions, every estimated region of attraction is subset of the next estimation. Finally, to show the effectiveness of the obtained results, some numerical examples are simulated.

This paper proposes a sliding mode observer (SMO) for actuator fault reconstruction of nonlinear systems subjected to external disturbance. In the proposed approach, first, the nonlinear system is modelled by a Takagi-Sugeno fuzzy model with immeasurable premise variables. Then, SMO is used to estimate the states and fault. Finally, by using a non-quadratic Lyapunov function (NQLF), the stability of the error system is proved. By considering performance criteria, the effect of the exogenous disturbance on the state estimations is minimized which provides effective fault estimation. Furthermore, the states and fault estimations asymptotically converge to their actual values for the non-perturbed systems. In the stability analysis and the observer gains design, some change of coordinates are proposed which the transformation matrix of one of them is obtained by solving linear matrix inequalities (LMIs). The proposed approach has some superiority over the existing methods. First, employing the NQLF leads to more relaxed results and better estimation performance. Second, using SMO for fault reconstruction makes the proposed approach insensitive to the uncertainties and unknown inputs and besides detecting the fault, its shape and size are determined. Third, since the premise variables are assumed to be unmeasurable, the presented approach is applicable for a wide class of nonlinear systems. Finally, a continuous stirred tank reactor (CSTR) process is considered and numerical simulation is carried out to illustrate the effectiveness and the accuracy of the proposed approach comparison with the recently published methods.

The perturbation method is an approximation scheme with a solvable leading order. The standard way is to choose a non-interacting sector for the leading order. The adaptive perturbation method improves the solvable part by using all diagonal elements for a Fock state. We consider the harmonic oscillator with the interacting term, λ1x4/6 + λ2x6/120, where λ1 and λ2 are coupling constants, and x is the position operator. The spectrum shows a quantitative result from the second-order, less than 1 percent error, compared to a numerical solution when turning off the λ2. When we turn on the λ2, more deviation occurs, but the error is still less than 2 percent. We show a quantitative result beyond a weak-coupling region. Our study should provide interest in the holographic principle and strongly coupled boundary theory.