Combined Model-Free Adaptive Control with Fuzzy Component by Virtual Reference Feedback Tuning for Tower Crane Systems

A novel mix of two data-driven algorithms is proposed in this paper. The mix of the algorithms aims to exploit the main advantage of data-driven Virtual Reference Feedback Tuning (VRFT) algorithm, that is represented by the automatic computation of the optimal parameters using a metaheuristic Grey W...

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Bibliographic Details
Published inProcedia computer science Vol. 162; pp. 267 - 274
Main Authors Roman, Raul-Cristian, Precup, Radu-Emil, Bojan-Dragos, Claudia-Adina, Szedlak-Stinean, Alexandra-Iulia
Format Journal Article
LanguageEnglish
Published Elsevier B.V 2019
Subjects
Data-driven algorithms
model-free adaptive fuzzy control
tower crane systems
virtual reference feedback tuning
Data-driven algorithms
model-free adaptive fuzzy control
tower crane systems
virtual reference feedback tuning
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Summary:A novel mix of two data-driven algorithms is proposed in this paper. The mix of the algorithms aims to exploit the main advantage of data-driven Virtual Reference Feedback Tuning (VRFT) algorithm, that is represented by the automatic computation of the optimal parameters using a metaheuristic Grey Wolf Optimizer (GWO) for the Compact Form Dynamic Linearization (CFDL) version of the authors’ Model-Free Adaptive Control Takagi-Sugeno Fuzzy Algorithm (CFDL-PDTSFA), so the parameters of the CFDL-PDTSFA are optimally tuned in a model-free manner via VRFT. Three specific optimization problems are defined and solved by Model-Free Adaptive Control, VRFT and GWO algorithms. The new resulted algorithm is validated using experimental results to the arm angular position of the nonlinear tower crane system laboratory equipment.
ISSN:1877-0509
1877-0509
DOI:10.1016/j.procs.2019.11.284