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Nonlinear System Identification of a Furuta Pendulum Using Machine Learning Techniques
Author(s) -
Rückwald Tobias,
Drücker Svenja,
Dücker Daniel-André,
Seifried Robert
Publication year - 2021
Publication title -
pamm
Language(s) - English
Resource type - Journals
ISSN - 1617-7061
DOI - 10.1002/pamm.202000036
Subject(s) - backlash , furuta pendulum , nonlinear system , control theory (sociology) , dynamical systems theory , computer science , identification (biology) , artificial neural network , nonlinear system identification , control engineering , pendulum , system identification , artificial intelligence , set (abstract data type) , focus (optics) , controller (irrigation) , nonlinear dynamical systems , inverted pendulum , double pendulum , engineering , data modeling , control (management) , physics , mechanical engineering , agronomy , botany , optics , quantum mechanics , database , biology , programming language
Usually, dynamical systems can be described by differential equations. An accurate model is essential when designing and optimizing a controller. However, not every system can be modeled easily by physical models due to highly nonlinear behavior, such as friction or backlash. Then, a data based approach, such as machine learning, might be helpful. The focus in this work is set on modeling dynamical systems using neural networks and deep learning, which are growing subjects in research and industry to identify nonlinear dynamics.