Theoretical and Natural Science
- The Open Access Proceedings Series for Conferences
Theoretical and Natural Science
Vol. 31, 07 March 2024
Open
Access | Article
Current study on PID control method with optimized BP neural network based on particle swarm optimization
* Author to whom correspondence should be addressed.
Abstract
At present, robots show great application value in various industries, and the progress of the industry constantly puts forward higher requirements for the performance of robots, and robot control is an important part of robot application. It is difficult for the traditional PID controller to implement online tuning when it is faced with actual objects. Consequently, the neural network algorithm has been included into robot control methods in recent years; the PID controller based on BP neural network is the subject of this research. This study presents the current state of research, the basic concept behind BP neural networks, how they are used in controller design, and how to optimize the PID parameters of BP neural networks using a particle swarm optimization method. To increase system stability, the neural network is coupled with a PID controller. The adaptive learning capability of the neural network is utilized to modify PID control parameters online in real time. Targeting the trouble that it slips into a local minimum easily during the BP-PID self-learning process and the refined PSO algorithm is used to improve it. It makes sure that the BP-PID system converges to the global optimal solution. The proposed method can effectively improve the system control accuracy and control stability.
Keywords
Neutral network, PID control algorithm, particle swarm optimization
References
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Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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- Volume Title
- Proceedings of the 3rd International Conference on Computing Innovation and Applied Physics
- ISBN (Print)
- 978-1-83558-317-3
- ISBN (Online)
- 978-1-83558-318-0
- Published Date
- 07 March 2024
- Series
- Theoretical and Natural Science
- ISSN (Print)
- 2753-8818
- ISSN (Online)
- 2753-8826
- DOI
- 10.54254/2753-8818/31/20241088
- Copyright
- 07 March 2024
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited