Comparison of Deep Learning and Deterministic Algorithms for Control Modeling

Controlling nonlinear dynamics arises in various engineering fields. We present efforts to model the forced van der Pol system control using physics-informed neural networks (PINN) compared to benchmark methods, including idealized nonlinear feedforward (FF) control, linearized feedback control (FB)...

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Vydané v:	Sensors (Basel, Switzerland) Ročník 22; číslo 17; s. 6362
Hlavní autori:	Zhai, Hanfeng, Sands, Timothy
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Basel MDPI AG 24.08.2022 MDPI
Predmet:	Algorithms Analysis Blended learning Complex systems Control theory Controllers Deep learning deterministic control Machine learning Neural networks nonlinear control Physics physics-informed neural networks Systems stability van der Pol dynamics United States
ISSN:	1424-8220, 1424-8220
On-line prístup:	Získať plný text
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