IJEER

The Emerging trends in PID controller design within the realm of Electrical and Electronics Engineering (EEE) underscore a shift towards advanced methodologies that enhance performance, adaptability, and efficiency across diverse applications. Traditional Proportional-Integral-Derivative (PID) controllers have long been the cornerstone of control systems, offering simplicity and effectiveness. However, with the advent of new technologies and the growing complexity of modern systems, several trends have surfaced, reshaping PID controller design. One prominent trend is the integration of machine learning techniques into PID control. Machine learning algorithms, particularly neural networks, enable controllers to adaptively learn system dynamics and optimize control parameters in real-time, offering superior performance in complex and nonlinear systems. This fusion of PID control with machine learning not only enhances robustness but also facilitates autonomous operation and fault detection, crucial in applications such as power electronics and robotics. Another trend is the utilization of fractional-order PID controllers. Unlike conventional integer-order PID controllers, fractional-order PID controllers incorporate fractional calculus concepts, enabling finer control over system dynamics. These controllers exhibit improved transient response, stability, and disturbance rejection, making them well-suited for applications with complex dynamics or non-integer-order processes, such as electrochemical systems and mechatronics. Furthermore, the emergence of Internet of Things (IoT) and Industry 4.0 has spurred developments in networked PID control systems. These systems leverage interconnected sensors, actuators, and computational devices to enable distributed control and data-driven optimization. By harnessing the vast amount of data generated by IoT-enabled devices, PID controllers can adaptively adjust parameters, predict system behavior, and optimize performance in real-time, enhancing efficiency and reliability in smart grid systems and industrial automation. Additionally, research in PID controller design has focused on addressing specific challenges posed by renewable energy systems, such as solar and wind power generation. Advanced PID control strategies tailored to the dynamic and stochastic nature of renewable energy sources aim to maximize energy extraction, improve grid integration, and enhance system stability. .

Topics of Interest

• Integration of Machine Learning in PID Control
• Fractional-Order PID Controllers for Nonlinear Systems
• Networked PID Control Systems in IoT and Industry 4.0
• Adaptive PID Control for Renewable Energy Systems
• PID Controller Design using Optimization
• Robust PID Control for Power Electronics
• Multi-Agent PID Control in Smart Grids
• Fault-tolerant PID Controllers for Industrial Automation
• Predictive PID Control for Dynamic Systems
• Real-time PID Control Strategies in Electric Vehicle Applications
• PID Control for Microgrid Stability Enhancement
• PID Control in Robotics: Motion Control and Path Planning

Important Dates

Submission and publication details	Timelines
Submission Deadline:	Sep 30, 2024
Notification of Acceptance:	December 15, 2024
Final Version Due:	January 25, 2025
Special Issue Publishing Date:	April, 2025
DOI, Indexing and Others	After 1 month of publication, will be automatically updated

Article Processing Charges

All papers submitted to Special Issues are subject to an Article Processing Charge (APC) if the manuscript is accepted for publication after peer review.

For more details on article processing charges, please follow the "Article Processing Charges" section of the journal for the Special Issue.

Chief Guest Editor

Guest Editor#1

Guest Editor#2

Proposals are accepted on a continuous basis till deadline for submission.

To submit, send your research paper to Vijayakumar.varadarajan@gmail.com