Focus and Scope

The Journal of Fuzzy Systems and Control should encompass original research articles, review articles, and case studies that contribute to the advancement of the theory and application of fuzzy systems and control, and their integration with other technologies, such as artificial intelligence, machine learning, and optimization. There is two main scopes which is Fuzzy Logic and Control System. This scope can be described in more detail as follows:

Fuzzy Logic

  1. Fuzzy systems: This includes research on the theory and applications of fuzzy logic, fuzzy sets, and fuzzy systems, such as fuzzy reasoning, fuzzy control, fuzzy optimization, fuzzy clustering, and fuzzy decision-making.
  2. Fuzzy control: This covers the development and application of fuzzy control methods for various systems, including industrial processes, robotics, automotive systems, aerospace systems, and power systems.
  3. Fuzzy optimization: This includes research on the development and application of fuzzy optimization methods, such as genetic algorithms, particle swarm optimization, and ant colony optimization, for solving complex problems in various fields.
  4. Fuzzy decision-making: This covers research on the development and application of fuzzy decision-making methods, such as fuzzy TOPSIS, fuzzy AHP, and fuzzy MCDM, for solving decision-making problems in various fields, such as finance, economics, and engineering.
  5. Fuzzy modeling and simulation: This includes research on the development and application of fuzzy modeling and simulation methods for various systems, such as complex systems, environmental systems, and biological systems.

Control System

  1. Control theory and design: This includes research on mathematical modeling, analysis, and design of control systems, covering topics such as stability, controllability, observability, and optimization.
  2. Control applications: This covers research on the application of control theory to practical engineering problems in various fields, including robotics, aerospace, automotive, chemical, and industrial systems.
  3. Intelligent control: This includes research on the integration of artificial intelligence techniques, such as fuzzy logic, neural networks, and evolutionary algorithms, into control systems to improve their performance and robustness.
  4. Nonlinear and adaptive control: This covers research on the design and analysis of control systems for nonlinear and time-varying systems, as well as systems with uncertainties, disturbances, and noise.
  5. Networked control systems: This includes research on the design and analysis of control systems that operate over communication networks, such as sensor networks and distributed control systems.
  6. Control education: This covers research on innovative approaches to teaching control theory and engineering, including the use of simulation tools and interactive learning environments.