Nonlinear Analysis:Hybrid Systems - Elsevier - Impact Factor

Nonlinear Analysis: Hybrid Systems - Elsevier | 2024 Impact Factor:4.1 | Cite Score:7.9 | Q1

Nonlinear Analysis: Hybrid Systems Journal - Elsevier

Impact Factor and Journal Rank of Nonlinear Analysis:Hybrid Systems

About: Nonlinear Analysis: Hybrid Systems is a peer-reviewed journal published by Elsevier. It focuses on research in the field of hybrid dynamical systems, providing a platform for researchers, academics, and practitioners to publish original research articles, reviews, and case studies that advance the understanding and application of nonlinear and hybrid systems theory.
Objective: The primary objective of Nonlinear Analysis: Hybrid Systems is to promote research and innovation in the analysis and control of hybrid dynamical systems. The journal aims to explore theories, methodologies, and applications that enhance the understanding and modeling of nonlinear and hybrid systems across various domains. By publishing high-quality research, the journal contributes to the advancement of hybrid systems theory and its impact on engineering, mathematics, and other related disciplines.
Interdisciplinary Focus: Nonlinear Analysis: Hybrid Systems adopts an interdisciplinary approach, welcoming contributions from various fields related to nonlinear and hybrid systems, including but not limited to, Control Theory, Dynamical Systems, Robotics, Electrical Engineering, Mechanical Engineering, Aerospace Engineering, Computer Science, Applied Mathematics, Computational Biology, Cyber-Physical Systems. This interdisciplinary perspective fosters collaboration and innovation, leading to the development of advanced techniques and solutions that address the complex challenges in analyzing and controlling nonlinear and hybrid systems.
Global Reach and Impact: With a broad international readership and authorship, Nonlinear Analysis: Hybrid Systems has a global reach and impact. Its publications contribute to the dissemination of knowledge and advancements in hybrid systems theory worldwide. The journal content influences both academic research and practical applications, driving progress in areas such as robotics, autonomous systems, biological modeling, and cyber-physical systems.
High Standards and Rigorous Review: Maintaining high academic standards, Nonlinear Analysis: Hybrid Systems conducts a rigorous peer-review process. Each submitted manuscript undergoes thorough evaluation by experts in the field to ensure the quality, originality, and scientific rigor of the research. This stringent review process upholds the integrity and reputation of the journal, ensuring that only high-quality and impactful research is published.
Significance: Nonlinear Analysis: Hybrid Systems plays a significant role in advancing research and practice in the analysis and control of nonlinear and hybrid systems. By providing a platform for the publication of cutting-edge research findings, the journal contributes to the growth of knowledge and innovation in hybrid systems theory and its applications. It serves as an essential resource for researchers, engineers, and practitioners seeking to leverage hybrid systems theory to address real-world challenges and develop novel solutions in diverse fields.

Journal Home: Journal Homepage

Editor-in-Chief: M. Heemels

Scope: The Nonlinear Analysis: Hybrid Systems journal, published by Elsevier, is a leading peer-reviewed publication that focuses on the theory and applications of hybrid dynamical systems. It provides a platform for researchers, engineers, and practitioners to share their innovative research findings and practical insights in this interdisciplinary field. Here is an overview of its key focus areas and scope:
1. Theory of Hybrid Dynamical Systems:
Research on the mathematical foundations, modeling frameworks, and analysis techniques for hybrid dynamical systems.
Studies on stability analysis, bifurcation theory, chaos theory, and control theory for hybrid systems.
2. Modeling and Simulation:
Advancements in modeling techniques and simulation methods for hybrid dynamical systems, including discrete-event systems, switched systems, and cyber-physical systems.
Research on numerical methods, symbolic methods, and software tools for analyzing and simulating hybrid systems.
3. Control and Optimization:
Exploration of control strategies, optimization algorithms, and decision-making techniques for hybrid dynamical systems.
Studies on optimal control, robust control, adaptive control, and reinforcement learning for hybrid systems.
4. Applications in Engineering:
Research on the application of hybrid dynamical systems theory in various engineering fields, including robotics, automotive systems, aerospace systems, power systems, and manufacturing systems.
Studies on modeling, analysis, and control of complex engineering systems with hybrid dynamics.
5. Applications in Biological Systems:
Exploration of the application of hybrid dynamical systems theory in modeling and analyzing biological systems, including gene regulatory networks, biochemical reaction networks, and neural systems.
Studies on the dynamics of biological systems under uncertainty, noise, and environmental perturbations.
6. Applications in Cyber-Physical Systems:
Research on the modeling, analysis, and control of cyber-physical systems (CPS) with hybrid dynamics, including autonomous vehicles, smart grids, healthcare systems, and smart cities.
Studies on the integration of physical processes with computational and communication components in CPS.
7. Applications in Autonomous Systems:
Exploration of the application of hybrid dynamical systems theory in autonomous systems, including unmanned aerial vehicles (UAVs), autonomous robots, and autonomous vehicles.
Research on motion planning, navigation, obstacle avoidance, and decision-making algorithms for autonomous systems.
8. Applications in Power Systems:
Research on the modeling, analysis, and control of power systems with hybrid dynamics, including renewable energy systems, microgrids, and smart grid networks.
Studies on stability analysis, control strategies, and optimization techniques for improving the performance and reliability of power systems.
9. Applications in Networked Systems:
Exploration of the application of hybrid dynamical systems theory in networked systems, including communication networks, transportation networks, and social networks.
Research on modeling, analysis, and control of networked systems with time-varying dynamics and interactions.
10. Emerging Trends and Future Directions:
Exploration of emerging trends and future directions in hybrid dynamical systems research, including new modeling paradigms, analysis techniques, and application domains.
Research on interdisciplinary collaborations, open research challenges, and opportunities for innovation in hybrid systems theory and practice.