Finite-Time/Fixed-Time Stability and Control of Dynamical Systems

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Dynamical Systems".

Deadline for manuscript submissions: 30 April 2025 | Viewed by 730

Special Issue Editors


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Guest Editor
School of Automation, China University of Geosciences, Wuhan 430074, China
Interests: memristive systems; dynamics of delayed neural networks; chaotic system encryption; finite-time control

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Guest Editor
College of Mathematics and System Science, Xinjiang University, Urumqi 830017, China
Interests: nonlinear complex systems; fractional-order system theory and application; neural networks dynamics and control

Special Issue Information

Dear Colleagues,

The traditional approach to stability analysis and control design for dynamical systems, often rooted in Lyapunov theory, has proven to be robust and versatile. However, in many modern applications, such as high-speed robotics, aerospace vehicles, and precision manufacturing systems, the requirement for convergence to equilibrium states within a finite time has emerged as a critical performance metric. This necessity has fueled the development of finite-time and fixed-time stability concepts, which offer the promise of faster and more predictable system responses.

The purpose of this Special Issue is to bring together experts from various disciplines, including control engineering, applied mathematics, and computer science, to discuss the latest advancements, challenges, and opportunities in enhancing the stability and control of dynamical systems within finite/fixed-time horizons. The objective is to foster interdisciplinary collaboration, stimulate innovative research, and promote the dissemination of knowledge and best practices in this rapidly evolving field.

We cordially invite researchers, practitioners, and academics working in this exciting area to submit original research articles, review papers, and case studies for consideration to this Special Issue. Contributions should focus on advancing the understanding of finite-time/fixed-time stability and the control of dynamical systems, either through theoretical developments, novel control strategies, or practical implementations. By participating in this Special Issue, contributing authors will have the opportunity to showcase their work to a broad and engaged audience and contribute to the ongoing progress in this critical research field.

Prof. Dr. Leimin Wang
Prof. Dr. Cheng Hu
Guest Editors

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Keywords

  • finite-time stability
  • fixed-time stability
  • finite-time synchronization
  • fixed-time synchronization
  • neural networks
  • complex networks
  • multi-agent systems
  • time-delay systems
  • control theory
  • control engineering

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Published Papers (1 paper)

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Research

16 pages, 1381 KiB  
Article
A Martingale Posterior-Based Fault Detection and Estimation Method for Electrical Systems of Industry
by Chao Cheng, Weijun Wang, He Di, Xuedong Li, Haotong Lv and Zhiwei Wan
Mathematics 2024, 12(20), 3200; https://doi.org/10.3390/math12203200 - 12 Oct 2024
Viewed by 580
Abstract
The improvement of information sciences promotes the utilization of data for process monitoring. As the core of modern automation, time-stamped signals are used to estimate the system state and construct the data-driven model. Many recent studies claimed that the effectiveness of data-driven methods [...] Read more.
The improvement of information sciences promotes the utilization of data for process monitoring. As the core of modern automation, time-stamped signals are used to estimate the system state and construct the data-driven model. Many recent studies claimed that the effectiveness of data-driven methods relies greatly on data quality. Considering the complexity of the operating environment, process data will inevitably be affected. This poses big challenges to estimating faults from data and delivers feasible strategies for electrical systems of industry. This paper addresses the missing data problem commonly in traction systems by designing a martingale posterior-based data generation method for the state-space model. Then, a data-driven approach is proposed for fault detection and estimation via the subspace identification technique. It is a general scheme using the Bayesian framework, in which the Dirichlet process plays a crucial role. The data-driven method is applied to a pilot-scale traction motor platform. Experimental results show that the method has good estimation performance. Full article
(This article belongs to the Special Issue Finite-Time/Fixed-Time Stability and Control of Dynamical Systems)
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