Dynamic Modeling, Calculation and Control of Complex Nonlinear Systems

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "C2: Dynamical Systems".

Deadline for manuscript submissions: 31 July 2026 | Viewed by 240

Special Issue Editor

School of Aeronautics, Harbin Institute of Technology, Harbin, China
Interests: complex nonlinear systems; control theory; dynamic analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Complex systems with many interacting parts are nonlinear, non-stationary and uncertain. These systems widely exist in nature, the social environment and industry. Understanding and mastering the operations of complex systems is very important for solving practical problems.

The focus of this Special Issue is on new and innovative methods for dynamic modeling, calculation and control of complex nonlinear systems. Topics of interest include, but are not limited to, the following:

  • Dynamic modeling, calculation, control of multibody systems, such as super-aperture antenna, cable-driven parallel robots, large liquid launch vehicles, and so on;
  • Autonomous dynamic assembly strategy of space structure based on multirobot systems in orbit;
  • Path-planning technology for spacecraft dynamic obstacle avoidance in orbit;
  • Management and control technology of giant constellations;
  • Missile interception penetration technology.

By showcasing the latest advancements in dynamic modeling, calculation and control of complex nonlinear systems, this Special Issue aims to promote the design and application of spacecraft in space environment.

Dr. Yi Ji
Guest Editor

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Keywords

  • dynamic and control of multibody systems
  • autonomous assembly of space structure
  • path-planning technology for spacecraft
  • management and control technology of giant constellations
  • missile interception penetration technology

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Published Papers (1 paper)

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Research

25 pages, 7707 KB  
Article
A Multi-Tier Vehicular Edge–Fog Framework for Real-Time Traffic Management in Smart Cities
by Syed Rizwan Hassan and Asif Mehmood
Mathematics 2025, 13(24), 3947; https://doi.org/10.3390/math13243947 - 11 Dec 2025
Viewed by 132
Abstract
The factors restricting the large-scale deployment of smart vehicular networks include application service placement/migration, mobility management, network congestion, and latency. Current vehicular networks are striving to optimize network performance through decentralized framework deployments. Specifically, the urban-level execution of current network deployments often fails [...] Read more.
The factors restricting the large-scale deployment of smart vehicular networks include application service placement/migration, mobility management, network congestion, and latency. Current vehicular networks are striving to optimize network performance through decentralized framework deployments. Specifically, the urban-level execution of current network deployments often fails to achieve the quality of service required by smart cities. To address these issues, we have proposed a vehicular edge–fog computing (VEFC)-enabled adaptive area-based traffic management (AABTM) architecture. Our design divides the urban area into multiple microzones for distributed control. These microzones are equipped with roadside units for real-time collection of vehicular information. We also propose (1) a vehicle mobility management (VMM) scheme to facilitate seamless service migration during vehicular movement; (2) a dynamic vehicular clustering (DVC) approach for the dynamic clustering of distributed network nodes to enhance service delivery; and (3) a dynamic microservice assignment (DMA) algorithm to ensure efficient resource-aware microservice placement/migration. We have evaluated the proposed schemes on different scales. The proposed schemes provide a significant improvement in vital network parameters. AABTM achieves reductions of 86.4% in latency, 53.3% in network consumption, 6.2% in energy usage, and 48.3% in execution cost, while DMA-clustering reduces network consumption by 59.2%, energy usage by 5%, and execution cost by 38.4% compared to traditional cloud-based urban traffic management frameworks. This research highlights the potential of utilizing distributed frameworks for real-time traffic management in next-generation smart vehicular networks. Full article
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