Composite Neural Dynamic Surface Control of a Class of Uncertain Nonlinear Systems in Strict-Feedback Form
- Submitting institution
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University of Plymouth
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 906
- Type
- D - Journal article
- DOI
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10.1109/tcyb.2014.2311824
- Title of journal
- IEEE Transactions on Cybernetics
- Article number
- -
- First page
- 2626
- Volume
- 44
- Issue
- 12
- ISSN
- 2168-2267
- Open access status
- Out of scope for open access requirements
- Month of publication
- April
- Year of publication
- 2014
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
3
- Research group(s)
-
-
- Citation count
- 229
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The control of uncertain or unknown technical systems like robots, vehicles, and other artefacts (especially under "uncertain" human operation) has an extremely wide field of potential applications. The present study significantly extends the scope of previous approaches in this area: By using an innovative neural network based approach it avoids the usage of often unknown analytic derivatives and it successfully tackles a computational complexity boundary that limits previous techniques. The work also presents rigorous error bounds most useful in real-world situations and, as a consequence, has extensive applications.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -