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An Enhanced Full-Form Model-Free Adaptive Controller for SISO Discrete-Time Nonlinear Systems
2022
Entropy
This study focuses on the full-form model-free adaptive controller (FFMFAC) for SISO discrete-time nonlinear systems, and proposes enhanced FFMFAC. The proposed technique design incorporates long short-term memory neural networks (LSTMs) and fuzzy neural networks (FNNs). To be more precise, LSTMs are utilized to adjust vital parameters of the FFMFAC online. Additionally, due to the high nonlinear approximation capabilities of FNNs, pseudo gradient (PG) values of the controller are estimated
doi:10.3390/e24020163
pmid:35205458
pmcid:PMC8871481
fatcat:gwcbqxmigrfhda7blha4qfdg3q