International Journal of Modelling, Identification and Control Inderscience Publishers Impact Factor

International Journal of Modelling, Identification and Control - Inderscience | 2025 Impact Factor: 0.5 | Cite Score:2.1 | SCOPUS Q4

International Journal of Modelling, Identification and Control With Cite Score

Cite Score and Journal Rank of International Journal of Modelling, Identification and Control

About: The International Journal of Modelling, Identification and Control (IJMIC) is a peer-reviewed journal that focuses on research in the fields of modeling, system identification, and control. It covers a broad range of topics including theoretical and practical aspects of system modeling, parameter estimation, control design, and system analysis. IJMIC aims to provide a platform for the dissemination of high-quality research that advances the understanding and application of these fundamental areas in control engineering and related fields.
Objective:
The primary objective of IJMIC is to advance the fields of modeling, identification, and control by publishing significant research findings, innovative methodologies, and practical applications. The journal seeks to facilitate the exchange of ideas and promote advancements in the development of models, identification techniques, and control strategies. IJMIC aims to support researchers, practitioners, and engineers in improving the performance and effectiveness of control systems through advanced theoretical and practical insights.
Interdisciplinary Approach:
IJMIC embraces an interdisciplinary approach, encouraging contributions from diverse fields such as control theory, signal processing, systems engineering, and applied mathematics. This approach ensures a comprehensive exploration of topics related to modeling, identification, and control, integrating diverse methodologies and perspectives. By promoting interdisciplinary research, the journal aims to address complex challenges and develop integrated solutions that advance the state of knowledge in these areas.
Impact:
The journal has a significant impact on both academic research and practical applications in the fields of modeling, identification, and control. It is widely cited by researchers, practitioners, and engineers interested in the latest developments and advancements in these areas. The research published in IJMIC contributes to the development of new models, identification methods, and control strategies that enhance the performance and efficiency of control systems. The journal serves as a valuable resource for professionals involved in the design, analysis, and implementation of control systems and technologies.
Significance:
IJMIC plays a crucial role in advancing the study and practice of modeling, identification, and control by providing a platform for high-quality research and practical insights. Its contributions support the development of innovative techniques and methodologies that address current and future challenges in control systems. The journals commitment to excellence and interdisciplinary focus make it an essential resource for anyone involved in research, development, and application in the fields of modeling, identification, and control. Through its rigorous scholarship and broad coverage, IJMIC helps shape the future of these fundamental areas in control engineering.

Journal Home: Journal Homepage

Editor-in-Chief: Dr. M.A. Dorgham

Scope: The International Journal of Modelling, Identification and Control (IJMIC) focuses on research related to the modeling, identification, and control of dynamic systems. Its scope includes, but is not limited to:
System Modeling: Research on techniques and methods for developing mathematical models of dynamic systems, including linear and nonlinear systems, continuous and discrete systems, and multi-variable systems.
System Identification: Studies on methods for identifying system parameters and structures from empirical data, including parameter estimation, system identification algorithms, and data-driven modeling.
Control Systems Design: Exploration of design methodologies for control systems, including classical control, modern control, robust control, adaptive control, and optimal control.
Nonlinear Control: Research on control strategies for nonlinear systems, including feedback linearization, sliding mode control, and nonlinear adaptive control.
Adaptive Control: Studies on control systems that adjust their parameters in real-time to accommodate changes in system dynamics or external conditions.
Robust Control: Research on control methods that ensure system performance and stability in the presence of uncertainties and disturbances.
Model Predictive Control (MPC): Exploration of predictive control techniques that use a model of the system to predict future behavior and optimize control actions over a specified time horizon.
Hybrid Systems: Studies on systems with both continuous and discrete dynamics, including control strategies for hybrid systems and analysis of their behavior.
Fault Detection and Diagnosis: Research on techniques for detecting and diagnosing faults or abnormalities in dynamic systems, including fault-tolerant control and system monitoring.
Time-Series Analysis: Exploration of methods for analyzing and forecasting time-series data, including signal processing techniques and statistical methods.
Simulation and Experimentation: Studies on the simulation and experimentation of dynamic systems, including the development of simulation tools and the design of experiments for system identification and control.
Applications: Research on the application of modeling, identification, and control techniques to various domains, including robotics, aerospace, automotive systems, process control, and manufacturing.
Distributed Control Systems: Exploration of control strategies for systems with distributed components, including networked control systems and decentralized control.
Optimization in Control: Studies on optimization techniques applied to control problems, including linear and nonlinear optimization, dynamic programming, and heuristic methods.
Theoretical Foundations: Research on the theoretical aspects of system modeling, identification, and control, including mathematical theories, stability analysis, and control system design principles.
Case Studies and Practical Implementations: Detailed case studies showcasing the application of modeling, identification, and control techniques to real-world problems and systems.
Emerging Technologies: Exploration of new and emerging technologies in the field of modeling and control, including advancements in algorithms, hardware, and applications.
The journal provides a platform for researchers, practitioners, and educators to share their work and advancements in the areas of system modeling, identification, and control, contributing to the development and application of these techniques in various fields.