Optimal control problems subject to uncertain random discrete-time noncausal systems
Chaos, Solitons & Fractals, ISSN: 0960-0779, Vol: 173, Page: 113604
2023
- 6Citations
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations6
- Citation Indexes6
- CrossRef3
Article Description
Uncertain random discrete-time noncausal systems (URDTNSs) and optimal control problems (OCPs) are investigated under the framework of chance theory with chance expectation in this paper. We convert OCPs subject to URDTNSs into conditional equivalent uncertain random OCPs employing an algebraic transformation method. Then recurrence equations are proposed accordingly to transform the uncertain random OCPs into solving problems for deterministic difference equations. The OCPs having linear and quadratic objective functions along with three kinds of URDTNSs involving linear, quadratic, and cubic control terms are studied. As a generalization of the main findings, we study two types of OCPs for time-delayed URDTNSs and give a unified framework for solving such problems. Meanwhile, analytical expressions of optimal solutions for these kinds of OCPs are acquired. Furthermore, numerical examples are offered to highlight the usefulness of our results.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S0960077923005052; http://dx.doi.org/10.1016/j.chaos.2023.113604; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85163057512&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S0960077923005052; https://dx.doi.org/10.1016/j.chaos.2023.113604
Elsevier BV
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