Iterative Learning Based Modulating Functions Method for Distributed Solar Source Estimation
KAUST DepartmentElectrical Engineering Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Computational Bioscience Research Center (CBRC)
KAUST Grant NumberBAS/1/1627-01-01
Permanent link to this recordhttp://hdl.handle.net/10754/666615
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AbstractModulating functions method is a non asymptotic estimation method, which provides accurate and robust estimations of states, parameters and inputs for different classes of systems, which include unknown linear ordinary differential systems, fractional systems and linear partial differential equations. In the case of time or space varying unknown, the method requires the decomposition of the unknown into predefined basis functions. However, the estimation performance will depend on the nature of the basis functions which in some cases are not easy to determine. This paper proposes a new iterative learning based modulating functions method, which combines the standard modulating functions with a dictionary learning procedure. The dictionary learning step allows the determination of appropriate set of functions to decompose the unknown, while the modulating function step allows the nonasymptotic and robust estimation of the projection coefficients. The performance of the proposed method is illustrated in a distributed solar collector application, modeled by partial differential equations and where the unknown solar irradiance is estimated.
CitationAljehani, F., & Laleg-Kirati, T.-M. (2021). Iterative Learning Based Modulating Functions Method for Distributed Solar Source Estimation. 2021 American Control Conference (ACC). doi:10.23919/acc50511.2021.9482958
SponsorsThis work has been supported by the King Abdullah University of Science and Technology (KAUST), Base Research Fund (BAS/1/1627-01-01) to Taous Meriem Laleg.
JournalIEEE Control Systems Letters
Conference/Event name2021 American Control Conference (ACC)