LAPSE:2021.0033
Published Article
LAPSE:2021.0033
Research on Improved Intelligent Control Processes Based on Three Kinds of Artificial Intelligence
February 22, 2021
Autotuning and online tuning of control parameters in control processes (OTP) are widely used in practice, such as in chemical production and industrial control processes. Better performance (such as dynamic speed and steady-state error) and less repeated manual-tuning workloads in bad environments for engineers are expected. The main works are as follows: Firstly, a change ratio for expert system and fuzzy-reasoning-based OTP methods is proposed. Secondly, a wavelet neural-network-based OTP method is proposed. Thirdly, comparative simulations are implemented in order to verify the performance. Finally, the stability of the proposed methods is analyzed based on the theory of stability. Results and effects are as follows: Firstly, the proposed control parameters of online tuning methods of artificial-intelligence-based classical control (AI-CC) systems had better performance, such as faster speed and smaller error. Secondly, stability was verified theoretically, so the proposed method could be applied with a guarantee. Thirdly, a lot of repeated and unsafe manual-based tuning work for engineers can be replaced by AI-CC systems. Finally, an upgrade solution AI-CC, with low cost, is provided for a large number of existing classical control systems.
Keywords
expert PID, fuzzy PID, intelligent control, online tuning for control parameters, wavelet neural network PID
Suggested Citation
Liu J, Li T, Chen J, Zuo F. Research on Improved Intelligent Control Processes Based on Three Kinds of Artificial Intelligence. (2021). LAPSE:2021.0033
Author Affiliations
Liu J: Information College, Capital University of Economics and Business, Beijing 100070, China; Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China [ORCID]
Li T: Information College, Capital University of Economics and Business, Beijing 100070, China [ORCID]
Chen J: Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China [ORCID]
Zuo F: Information College, Capital University of Economics and Business, Beijing 100070, China
Journal Name
Processes
Volume
8
Issue
9
Article Number
E1042
Year
2020
Publication Date
2020-08-26
Published Version
ISSN
2227-9717
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Original Submission
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PII: pr8091042, Publication Type: Journal Article
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LAPSE:2021.0033
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doi:10.3390/pr8091042
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Feb 22, 2021
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Feb 22, 2021
 
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Original Submitter
Calvin Tsay
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