Adaptive Neural Network Feedforward Control for Dynamically Substructured Systems
- Submitting institution
-
Queen Mary University of London
- Unit of assessment
- 12 - Engineering
- Output identifier
- 557
- Type
- D - Journal article
- DOI
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10.1109/TCST.2013.2271036
- Title of journal
- IEEE Transactions on Control Systems Technology
- Article number
- -
- First page
- 944
- Volume
- 22
- Issue
- 3
- ISSN
- 1063-6536
- Open access status
- Out of scope for open access requirements
- Month of publication
- May
- 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
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3
- Research group(s)
-
-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- A novel online adaptive neural network learning law is proposed to tackle uncertainties and nonlinearities in dynamically substructured systems (DSS), which is an important control problem in hybrid testing community. The result significantly simplifies the DSS applications in a broad range of engineering problems and provides an economically viable dynamics testing environment. Real-time experiments confirmed its efficacy. This is a key research output from an EPSRC grant (EP/D036917/1, £493K, 2007-2010), and led to securing the Royal Society Advanced Newton Fellowship (NA160436, £111K, 2017-2020) and ESPRC grant (EP/P023002/1, £304K, 2017-2020).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -