Algorithms for PID Controller 2023
A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms for Multidisciplinary Applications".
Deadline for manuscript submissions: closed (29 February 2024) | Viewed by 4969
Special Issue Editors
Interests: control; simulation; optimization; fractional calculus; evolutionary algorithms; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Interests: control engineering; evolutionary and nature inspired algorithms for single and multiple objective optimization problem solving
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
To date, the PID controller is still the most commonly used algorithm for control applications. Since its first development, the PID algorithm has gone hand in hand with the evolution of science and engineering, and new methods and applications have been introduced over time. Advances in recent decades, provided by the area of fractional calculus and metaheuristic algorithms, and, more recently, by artificial intelligence, have given rise to a refreshing boost to PID control.
This Special Issue aims to present the most recent developments in the design, tuning, and applications of PID controllers. The focus is on reporting theoretical and applied research results in control structures, optimization techniques, metaheuristic algorithms, tuning methods, digital implementations, and applications of the PID algorithm, among others, and the use of current artificial intelligence techniques, such as machine learning, deep learning, and reinforcement learning.
Prof. Dr. Ramiro Barbosa
Dr. Paulo Moura Oliveira
Guest Editors
Manuscript Submission Information
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Keywords
- fractional-order PID controller
- fuzzy PID controller
- neural PID controller
- fuzzy logic
- fractional-order control
- predictive control
- optimization
- neural networks
- metaheuristic algorithms
- neural-fuzzy algorithms
- evolutionary algorithms
- machine learning
- deep learning
- digital implementation
- reinforcement learning
- artificial intelligence
