A sampled-data control problem of neural-network-based systems using an improved free-matrix-based inequality. Issue 15 (October 2019)

• Record Type: Journal Article
• Authors: Lee, S.H.; Park, M.J.; Kwon, O.M.; Sakthivel, R.
• Is Part Of: Journal of the Franklin Institute. Volume 356:Issue 15(2019)
• Page Start: 8344
• Page End: 8365
• Publication Date: 2019-10
• Subjects: Science -- Periodicals; Technology -- Periodicals; Patents -- United States -- Periodicals; 505
• DOI: 10.1016/j.jfranklin.2019.08.001
• Languages: English
• ISSNs: 0016-0032

Abstract

Abstract: In this work, a sampled-data control problem for neural-network-based systems with an optimal guaranteed cost is investigated. By constructing suitable time-dependent functionals and utilizing an improved free-matrix-based integral inequality, a sampled-data stability criterion for neural-network-based systems is derived. Based on a first result, a sampled-data controller design method for neural-network-based systems that meets the maximum sampling period and minimum guaranteed cost performance is proposed. The superiority and validity of the results will be verified by comparing with the existing results in a numerical example.