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Research on Improved Intelligent Control Processes Based on Three Kinds of Artificial Intelligence
- Source :
- Processes, Volume 8, Issue 9, Processes, Vol 8, Iss 1042, p 1042 (2020)
- Publication Year :
- 2020
- Publisher :
- Multidisciplinary Digital Publishing Institute, 2020.
-
Abstract
- 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.
- Subjects :
- 0209 industrial biotechnology
Computer science
Control (management)
Stability (learning theory)
Bioengineering
02 engineering and technology
online tuning for control parameters
computer.software_genre
lcsh:Chemical technology
lcsh:Chemistry
020901 industrial engineering & automation
Wavelet
0202 electrical engineering, electronic engineering, information engineering
Chemical Engineering (miscellaneous)
lcsh:TP1-1185
expert PID
Process Chemistry and Technology
Work (physics)
wavelet neural network PID
Control engineering
fuzzy PID
Expert system
Upgrade
lcsh:QD1-999
Control system
020201 artificial intelligence & image processing
Intelligent control
intelligent control
computer
Subjects
Details
- Language :
- English
- ISSN :
- 22279717
- Database :
- OpenAIRE
- Journal :
- Processes
- Accession number :
- edsair.doi.dedup.....e3fb531d0c6f058a42130f28d680a6fd
- Full Text :
- https://doi.org/10.3390/pr8091042