Enhancing PID Control with Neural Network Integration: Analysis of RBF, BP, and Fuzzy Neural Network Models

DOI

10.2991/978-94-6463-518-8_55How to use a DOI?

Keywords

Neural Network; PID Control; Integration

Abstract

Traditional PID controllers, despite their widespread use due to simplicity and robustness, often falter in handling nonlinearities and time-varying systems without frequent retuning. Since scholars are not satisfied with conventional control theory, an integration of neural network and PID controlled have been explored and new control theory is constructed. The advent of neural networks offers a dynamic enhancement to PID controllers by introducing adaptive capabilities, self-learning, and fault tolerance. With the assistance of neural network, PID controllers have gained new method to automatically control the target. This paper aims to presents a comprehensive review of the integration of neural network technologies with Proportional-Integral-Derivative (PID) controllers, emphasizing their application in complex and nonlinear control systems. The design, operation, and application domains of a number of neural network models, including Radial Basis Function (RBF), Backpropagation (BP), and Fuzzy Neural Network PID controllers, are examined. These neural network-based PID controllers have shown considerable success in diverse sectors including robotics, process control, and environmental systems, reflecting improved performance over traditional methods. This paper not only outlines the operational principles and advancements in neural network PID controllers but also discusses the challenges and future prospects for further enhancement of feedback control mechanisms.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Yuning Li
PY  - 2024
DA  - 2024/09/28
TI  - Enhancing PID Control with Neural Network Integration: Analysis of RBF, BP, and Fuzzy Neural Network Models
BT  - Proceedings of the 2024 International Conference on Mechanics, Electronics Engineering and Automation (ICMEEA 2024)
PB  - Atlantis Press
SP  - 582
EP  - 593
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-518-8_55
DO  - 10.2991/978-94-6463-518-8_55
ID  - Li2024
ER  -