Event-Triggered Compound Learning Tracking Control of Nonstrict-Feedback Nonlinear Systems in Sensor-to-Controller Channel Article Swipe
Yingjie Deng
,
Tao Ni
,
Jiantao Wang
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-372665/v1
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-372665/v1
This paper investigates the event-triggered tracking control of the nonstrict-feedback nonlinear system with the time-varying disturbances. While the fuzzy logic systems (FLSs) serve as the approximators to the unknown dynamics, the compound disturbance is comprised of the time-varying disturbance and the approximation error of the FLS. An event-triggered compound learning algorithm is originally developed to accurately estimate the total uncertainties. By referring to an event-triggered adaptive model, the control laws are derived without provoking the problem of "algebraic loop", seeing Remark 3. The command filters are employed to generate the continuous substitutes for both the virtual control laws and their derivatives, so as to solve the recently proposed problem of "jumps of virtual control laws" arising in the backstepping-based event-triggered control (ETC) that functions in the channel of sensor to controller. The triggering condition is constructed to guarantee the similarity between the adaptive model and the original system. While the satisfactory learning performance of the FLSs and the compound disturbances estimation are maintained, the proposed control scheme can guarantee the semi-globally uniformly ultimate boundedness (SGUUB) of all the tracking errors. Finally, a numerical experiment is carried out to exemplify the effectiveness of the proposed control scheme.
Related Topics
Concepts
Control theory (sociology)
Tracking (education)
Controller (irrigation)
Nonlinear system
Control (management)
Channel (broadcasting)
Computer science
Event (particle physics)
Feedback control
Control system
Control engineering
Artificial intelligence
Engineering
Psychology
Physics
Telecommunications
Electrical engineering
Pedagogy
Agronomy
Quantum mechanics
Biology
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-372665/v1
- https://www.researchsquare.com/article/rs-372665/v1.pdf?c=1636748473000
- OA Status
- gold
- References
- 57
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4248356821
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4248356821Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.21203/rs.3.rs-372665/v1Digital Object Identifier
- Title
-
Event-Triggered Compound Learning Tracking Control of Nonstrict-Feedback Nonlinear Systems in Sensor-to-Controller ChannelWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-04-06Full publication date if available
- Authors
-
Yingjie Deng, Tao Ni, Jiantao WangList of authors in order
- Landing page
-
https://doi.org/10.21203/rs.3.rs-372665/v1Publisher landing page
- PDF URL
-
https://www.researchsquare.com/article/rs-372665/v1.pdf?c=1636748473000Direct 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.researchsquare.com/article/rs-372665/v1.pdf?c=1636748473000Direct OA link when available
- Concepts
-
Control theory (sociology), Tracking (education), Controller (irrigation), Nonlinear system, Control (management), Channel (broadcasting), Computer science, Event (particle physics), Feedback control, Control system, Control engineering, Artificial intelligence, Engineering, Psychology, Physics, Telecommunications, Electrical engineering, Pedagogy, Agronomy, Quantum mechanics, BiologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
57Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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