LSTM-CNN Network-Based State-Dependent ARX Modeling and Predictive Control with Application to Water Tank System Article Swipe
Tiao Kang
,
Hui Peng
,
Xiaoyan Peng
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/act12070274
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/act12070274
Industrial process control systems commonly exhibit features of time-varying behavior, strong coupling, and strong nonlinearity. Obtaining accurate mathematical models of these nonlinear systems and achieving satisfactory control performance is still a challenging task. In this paper, data-driven modeling techniques and deep learning methods are used to accurately capture a category of a smooth nonlinear system’s spatiotemporal features. The operating point of these systems may change over time, and their nonlinear characteristics can be locally linearized. We use a fusion of the long short-term memory (LSTM) network and convolutional neural network (CNN) to fit the coefficients of the state-dependent AutoRegressive with the eXogenous variable (ARX) model to establish the LSTM-CNN-ARX model. Compared to other models, the hybrid LSTM-CNN-ARX model is more effective in capturing the nonlinear system’s spatiotemporal characteristics due to its incorporation of the strengths of LSTM for learning temporal characteristics and CNN for capturing spatial characteristics. The model-based predictive control (MPC) strategy, namely LSTM-CNN-ARX-MPC, is developed by utilizing the model’s local linear and global nonlinear features. The control comparison experiments conducted on a water tank system show the effectiveness of the developed models and MPC methods.
Related Topics
Concepts
Nonlinear system
Computer science
Convolutional neural network
Autoregressive model
Model predictive control
Artificial intelligence
Deep learning
Artificial neural network
Reservoir computing
Control theory (sociology)
Nonlinear autoregressive exogenous model
Process (computing)
State (computer science)
State variable
Pattern recognition (psychology)
Machine learning
Control engineering
Recurrent neural network
Control (management)
Algorithm
Engineering
Mathematics
Physics
Operating system
Thermodynamics
Quantum mechanics
Econometrics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/act12070274
- https://www.mdpi.com/2076-0825/12/7/274/pdf?version=1688650271
- OA Status
- gold
- Cited By
- 2
- References
- 57
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4383535638
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4383535638Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/act12070274Digital Object Identifier
- Title
-
LSTM-CNN Network-Based State-Dependent ARX Modeling and Predictive Control with Application to Water Tank SystemWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-07-06Full publication date if available
- Authors
-
Tiao Kang, Hui Peng, Xiaoyan PengList of authors in order
- Landing page
-
https://doi.org/10.3390/act12070274Publisher landing page
- PDF URL
-
https://www.mdpi.com/2076-0825/12/7/274/pdf?version=1688650271Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/2076-0825/12/7/274/pdf?version=1688650271Direct OA link when available
- Concepts
-
Nonlinear system, Computer science, Convolutional neural network, Autoregressive model, Model predictive control, Artificial intelligence, Deep learning, Artificial neural network, Reservoir computing, Control theory (sociology), Nonlinear autoregressive exogenous model, Process (computing), State (computer science), State variable, Pattern recognition (psychology), Machine learning, Control engineering, Recurrent neural network, Control (management), Algorithm, Engineering, Mathematics, Physics, Operating system, Thermodynamics, Quantum mechanics, EconometricsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 1Per-year citation counts (last 5 years)
- References (count)
-
57Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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