Output Feedback-Based Neural Network Sliding Mode Control for Electro-Hydrostatic Systems with Unknown Uncertainties Article Swipe
Tri Dung Dang
,
Tri Cuong
,
Hoai Vu Anh Truong
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/machines12080554
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/machines12080554
This paper proposes an output feedback-based control for uncertain electro-hydrostatic systems (EHSs) to satisfy high output tracking precision under the influences of unknown mismatched and matched uncertainties and unstructured dynamical behavior. In this configuration, an extended state observer (ESO) is first employed to obtain unmeasured states and suppress the adverse effect of matched uncertainty. Meanwhile, the influence of unstructured dynamical behavior is approximated by employing a radial basis function neural network (RBFNN)-based technique. With the unmeasured states observed, matched uncertainty, and system dynamics compensated, the robust backstepping sliding mode control is accordingly established and the lumped mismatched uncertainty is then suppressed through disturbance observer-based adaptive law. Interestingly, the proposed control methodology requires only output feedback but can address the whole system dynamics. The stability of the closed-loop system is theoretically proven through a Lyapunov theorem and the effectiveness of the proposed methodology is demonstrated through comparative simulations.
Related Topics
Concepts
Control theory (sociology)
Backstepping
State observer
Lyapunov function
Observer (physics)
Computer science
Sliding mode control
Lyapunov stability
Artificial neural network
Control engineering
Adaptive control
Nonlinear system
Control (management)
Engineering
Physics
Artificial intelligence
Quantum mechanics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/machines12080554
- OA Status
- gold
- References
- 39
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4401536541
All OpenAlex metadata
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https://openalex.org/W4401536541Canonical identifier for this work in OpenAlex
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https://doi.org/10.3390/machines12080554Digital Object Identifier
- Title
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Output Feedback-Based Neural Network Sliding Mode Control for Electro-Hydrostatic Systems with Unknown UncertaintiesWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-08-13Full publication date if available
- Authors
-
Tri Dung Dang, Tri Cuong, Hoai Vu Anh TruongList of authors in order
- Landing page
-
https://doi.org/10.3390/machines12080554Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.3390/machines12080554Direct OA link when available
- Concepts
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Control theory (sociology), Backstepping, State observer, Lyapunov function, Observer (physics), Computer science, Sliding mode control, Lyapunov stability, Artificial neural network, Control engineering, Adaptive control, Nonlinear system, Control (management), Engineering, Physics, Artificial intelligence, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W2948179959, https://openalex.org/W4391071560, https://openalex.org/W3113855841, https://openalex.org/W4225884659, https://openalex.org/W3006189556, https://openalex.org/W4294310841, https://openalex.org/W4390949127, https://openalex.org/W4388345074, https://openalex.org/W4310038679, https://openalex.org/W2085470933, https://openalex.org/W3047508845, https://openalex.org/W4392752882, https://openalex.org/W4389954563, https://openalex.org/W2076745509, https://openalex.org/W4361279161, https://openalex.org/W4317623156, https://openalex.org/W2893573014, https://openalex.org/W2810461391, https://openalex.org/W4304136188, https://openalex.org/W4392694546, https://openalex.org/W3187874522, https://openalex.org/W2979685018, https://openalex.org/W3111351152, https://openalex.org/W3112106307, https://openalex.org/W4229444447, https://openalex.org/W4390578037, https://openalex.org/W2056849404, https://openalex.org/W2971428107, https://openalex.org/W3089811306, https://openalex.org/W2982187281, https://openalex.org/W4363672460, https://openalex.org/W3011098145, https://openalex.org/W4387682224, https://openalex.org/W4385308566, https://openalex.org/W4394870859, https://openalex.org/W3129392980, https://openalex.org/W2965744298, https://openalex.org/W3036442660, https://openalex.org/W2045013876 |
| referenced_works_count | 39 |
| abstract_inverted_index.a | 65, 132 |
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| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5039864531, https://openalex.org/A5007201850 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 3 |
| corresponding_institution_ids | https://openalex.org/I123900574, https://openalex.org/I181727666 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[0].score | 0.7900000214576721 |
| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile.value | 0.16372418 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |