Neuro-Optimal Event-Triggered Impulsive Control for Stochastic Systems via ADP Article Swipe
Mingming Liang
,
Derong Liu
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1109/tnnls.2022.3232635
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1109/tnnls.2022.3232635
This article presents a novel neural-network-based optimal event-triggered impulsive control method. First, a novel general-event-based impulsive transition matrix (GITM) is constructed to represent the probability distribution evolving characteristics regarding all system states across the impulsive actions, rather than the prefixed timing sequence. On the foundation of this GITM, the event-triggered impulsive adaptive dynamic programming (ETIADP) algorithm and its high-efficiency version (HEIADP) are developed to deal with the optimization problems for stochastic systems with event-triggered impulsive controls. It is shown that the obtained controller design scheme can reduce the computational and communication burden caused by updating the controller periodically. By analyzing the admissibility, monotonicity, and optimality properties of ETIADP and HEIADP, we further establish the approximation error bound of the neural networks to address the connection between the ideal and neural-network-based realizations of the present methods. It is proven that the iterative value functions of both the ETIADP and HEIADP algorithms fall in a small neighborhood of the optimum as the iteration index increases to infinity. By adopting a novel task synchronization mechanism, the proposed HEIADP algorithm fully utilizes the computing resources of multiprocessor systems (MPSs), while significantly reducing the memory requirement compared to traditional ADP approaches. Finally, we carry out a numerical study to show that the proposed methods can fulfill the desired goals.
Related Topics
Concepts
Computer science
Artificial neural network
Controller (irrigation)
Monotonic function
Dynamic programming
Bellman equation
Mathematical optimization
Optimal control
Synchronization (alternating current)
Event (particle physics)
Sequence (biology)
Control theory (sociology)
Mathematics
Algorithm
Control (management)
Artificial intelligence
Agronomy
Channel (broadcasting)
Quantum mechanics
Computer network
Physics
Biology
Mathematical analysis
Genetics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/tnnls.2022.3232635
- https://ieeexplore.ieee.org/ielx7/5962385/6104215/10008206.pdf
- OA Status
- hybrid
- Cited By
- 9
- References
- 44
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4313598833
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4313598833Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/tnnls.2022.3232635Digital Object Identifier
- Title
-
Neuro-Optimal Event-Triggered Impulsive Control for Stochastic Systems via ADPWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
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2023-01-06Full publication date if available
- Authors
-
Mingming Liang, Derong LiuList of authors in order
- Landing page
-
https://doi.org/10.1109/tnnls.2022.3232635Publisher landing page
- PDF URL
-
https://ieeexplore.ieee.org/ielx7/5962385/6104215/10008206.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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hybridOpen access status per OpenAlex
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https://ieeexplore.ieee.org/ielx7/5962385/6104215/10008206.pdfDirect OA link when available
- Concepts
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Computer science, Artificial neural network, Controller (irrigation), Monotonic function, Dynamic programming, Bellman equation, Mathematical optimization, Optimal control, Synchronization (alternating current), Event (particle physics), Sequence (biology), Control theory (sociology), Mathematics, Algorithm, Control (management), Artificial intelligence, Agronomy, Channel (broadcasting), Quantum mechanics, Computer network, Physics, Biology, Mathematical analysis, GeneticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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9Total citation count in OpenAlex
- Citations by year (recent)
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2025: 4, 2024: 1, 2023: 4Per-year citation counts (last 5 years)
- References (count)
-
44Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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