Advanced PID Control: Theory and Applications Towards a Smarter PID Controller

Dear Colleagues,

PID Controllers are ubiquitous in industrial applications, covering more than 95% of control needs due to their flexibility with regard to tuning, with hundreds of methods available (heuristics/robust/optimal/adaptive/model-based); their direct implementation on embedded platforms; and their straightforward integration with new and existing systems that require feedback controls to achieve robust and optimal performance in the presence of external disturbances and uncertainties.

Although the term ‘PID’ was first used in 1922, it remains relevant today. Industry 4.0 and digital transformation are changing classic control applications into smart systems owing to the increased availability of data that has resulted from the integration of enabling technologies such as artificial intelligence, big data, edge computing, or the industrial internet of things (IIoT).

This Special Issue, entitled “Advanced PID Control: Theory and Applications towards a Smarter PID Controller”, invites researchers and users of control systems in academia and industry to provide an overview of recent developments in PID controllers and their applications.

The aim of this Special Issue is to demonstrate how the classical PID controller, with all its variants and features (e.g., Antiwindup, Tracking control, Feedforward, disturbance rejection observer, smith predictors, adaptive gains, prefilters, cascade, ratio), remains a critical component of the edge technology employed in fields such as semiconductor manufacturing, battery management, energy storage, smart grids, unmanned aerial vehicles, self-driving cars, aeronautics or satellite communications.

The scope of this Special Issue includes, but is not limited to, the following topics:

• Integration of enabling technologies (Machine Learning, Digital Twin, Big Data, Data Analytics) with PID controllers to enhance its performance and robustness;
• Built-in environmental awareness through big data-informed PID controllers;
• Development of new control performance assessment methods for PID controllers;
• SISO and MIMO PID controllers applications used on critical industrial manufacturing equipment (e.g., semiconductors, autonomous navigation, food processing);
• Development of PID controller tuning methods based on data-driven approaches (e.g., VRFT, Dynamic Mode Decomposition (DMD), extremum seeking);
• Machine-learning-informed PID controllers with fault detection and remaining useful life awareness;
• Self-optimizing PID controllers based on real-time system identification or sequential high-order optimization methods;
• Recent methods and findings regarding integer and fractional-order PID controllers.

Dr. Jairo Viola
Guest Editor

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• digital twin
• self-optimizing control
• fractional control
• industrial robotics
• process control