Year: 2024
Type: article
Abstract: This study explores the implementation and efficacy of a neural network controller for an inverted pendulum system, contrasting it with traditional state feedback control. Initially, state feedback control exhibited limitations in managing complex system dynamics. Subsequently, a neural network controller was developed, trained using datasets from both uncontrolled and refined state space models. The refined model yielded lower training loss and superior control performance. This research demonstrates the neural network controller’s enhanced adaptability and precision, offering significant improvements over traditional methods in controlling dynamic systems like inverted pendulums.
Authors: Yusen Xie, Yingjie Mi
Institution: The University of Sydney
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4404249977