International Journal of Automation and Control

Scope & Guideline

Connecting Academia and Industry: Pioneering Solutions in Automation and Control.

Introduction

Immerse yourself in the scholarly insights of International Journal of Automation and Control with our comprehensive guidelines detailing its aims and scope. This page is your resource for understanding the journal's thematic priorities. Stay abreast of trending topics currently drawing significant attention and explore declining topics for a full picture of evolving interests. Our selection of highly cited topics and recent high-impact papers is curated within these guidelines to enhance your research impact.
LanguageEnglish
ISSN1740-7516
PublisherINDERSCIENCE ENTERPRISES LTD
Support Open AccessNo
CountryUnited Kingdom
TypeJournal
Convergefrom 2007 to 2024
AbbreviationINT J AUTOM CONTROL / Int. J. Autom. Control
Frequency4 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
AddressWORLD TRADE CENTER BLDG, 29 ROUTE DE PRE-BOIS, CASE POSTALE 856, CH-1215 GENEVA, SWITZERLAND

Aims and Scopes

The International Journal of Automation and Control focuses on advancing the field of automation through innovative research and application of control systems. Its core areas encompass various methodologies and technologies that enhance automation across diverse industries, particularly in robotics, energy management, and intelligent systems.
  1. Automation and Robotics:
    Research on the design and control of robotic systems, including autonomous vehicles and surgical robots, emphasizing intelligent control strategies and real-time decision-making.
  2. Control Theory and Applications:
    Development and implementation of control strategies such as sliding mode control, PID control, and model predictive control for various engineering applications.
  3. Energy Management Systems:
    Innovative approaches to managing energy systems, particularly in renewable energy sources and electric vehicles, focusing on optimization and control techniques.
  4. Intelligent Systems and Machine Learning:
    Integration of machine learning algorithms with control systems for fault detection, system identification, and adaptive control, enhancing system performance and reliability.
  5. Simulation and Modeling:
    Use of simulation-based methodologies to model complex systems and evaluate control strategies, providing a platform for testing and validation of theoretical approaches.
The recent publications in the International Journal of Automation and Control reveal several emerging themes that are gaining traction. These trends indicate a shift towards more advanced methodologies and applications in automation and control.
  1. Real-Time Control Systems:
    There is a marked increase in research focused on real-time control systems for robotics and autonomous vehicles, emphasizing the need for immediate data processing and decision-making capabilities.
  2. Neural Network and AI Integration:
    The integration of neural networks and artificial intelligence in control systems is on the rise, with studies exploring how these technologies can enhance adaptive control and system performance.
  3. Sustainable Energy Solutions:
    Research on energy management systems, particularly in renewable energy applications, is trending as industries seek sustainable solutions to energy challenges, including smart grid technologies.
  4. Advanced Sliding Mode Control Techniques:
    An emerging focus on novel sliding mode control strategies tailored for complex systems indicates a growing interest in robust control methods that can handle uncertainties and disturbances.
  5. Bio-Inspired Control Strategies:
    There is an increasing trend towards bio-inspired approaches in control systems, utilizing strategies derived from natural systems for enhanced adaptability and efficiency in robotics and automation.

Declining or Waning

In recent years, certain themes within the International Journal of Automation and Control have shown signs of declining prominence. This may reflect shifts in research focus or advancements in technology that render previous approaches less relevant.
  1. Traditional Control Techniques:
    While control techniques like classical PID controllers remain important, there is a noticeable decline in papers focusing exclusively on these methods, as the field shifts towards more adaptive and intelligent control strategies.
  2. Generic Optimization Algorithms:
    Research centered around traditional optimization algorithms, such as basic genetic algorithms or particle swarm optimization without specific enhancements, is becoming less frequent as more sophisticated, problem-specific methods gain traction.
  3. Basic Fault Detection Methods:
    The emphasis on conventional fault detection techniques is waning as researchers increasingly adopt advanced machine learning approaches that provide more robust and adaptive solutions.

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