Showing posts with label Lyapunov functions. Show all posts
Showing posts with label Lyapunov functions. Show all posts

Friday, 22 August 2025

Stabilization in Discrete Nonlinear Systems Using Lyapunov Exponent | Chapter 10 | Mathematics and Computer Science: Contemporary Developments Vol. 1

 

We deal with the stabilization problem of discrete nonlinear systems. We construct a control Lyapunov function on discrete nonlinear systems. Then, we present a new method to construct a continuous state feedback law.


Author(s) Details

Fengjun Tang
Nanhang Jincheng College, Nanjing University of Aeronautics and Astronautics, China.

Rong Yuan
Nanhang Jincheng College, Nanjing University of Aeronautics and Astronautics, China.

 

Please see the book here:- https://doi.org/10.9734/bpi/mcscd/v1/7126E

Saturday, 5 November 2022

Stability Investigation of Systems of Nonlinear Stochastic Difference Equations| Chapter 6 | Research Highlights in Mathematics and Computer Science Vol. 2

 The aim concerning this research is to present a method for establishment investigation of systems of nonlinear distinctness equations influenced by various types of stochastic perturbations. The projected method is demonstrated on the strength investigation of a nonlinear discrete-opportunity model of information dissemination. We get the conditions for this system's certain equilibrium. It is demonstrated that drunk of stochastic perturbations of the various forms, containing minor multiplicative perturbations, rapidly fading multiplicative perturbations, and promptly fading preservative perturbations, asymptotically stable positive balance maintains its balance. Through the use of Lyapunov functions, linear matrix prejudices (LMIs), and numerical simulations of the system existing, stability requirements are driven. It is suggested to look at the synopsis where stochastic disturbances dissolve on the infinity, but not very fast, as an unresolved problem. The deliberate here method of strength investigation can be used for many other nonlinear mathematical models interpreted both by stochastic dissimilarity equations and by stochastic characteristic equations.

Author(s) Details:

Leonid Shaikhet,
Department of Mathematics, Ariel University, Ariel-40700, Israel.

Please see the link here: https://stm.bookpi.org/RHMCS-V2/article/view/8573