Neural network ARMAX model for a Furuta pendulum
ABSTRACT The rotational inverted pendulum or Furuta Pendulum is a mechatronic system used by control engineers to explore a various dynamic modeling and control schemes. Due to its nonlinear nature, open-loop instability, and because it is an under-actuated system (more degrees of freedom than actuators), which is the basis for the design of vehicles such as the Segway, the self-balancing scooter, hoverboard, or self-balancing board, among others. The authors present a model for the Furuta Pendulum using the equations of Euler-Lagrange and the methodology to identify a black-box model by training an NNARMAX (Neural Network Auto-Regressive Moving Average with exogenous inputs). The results show that two interconnected MISO-NNARMAX estimates 10-step-ahead predictions accurately for the horizontal and vertical angles.
| Main Authors: | , , , |
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| Format: | Digital revista |
| Language: | English |
| Published: |
Universidad de Tarapacá.
2021
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| Online Access: | http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-33052021000400668 |
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| Summary: | ABSTRACT The rotational inverted pendulum or Furuta Pendulum is a mechatronic system used by control engineers to explore a various dynamic modeling and control schemes. Due to its nonlinear nature, open-loop instability, and because it is an under-actuated system (more degrees of freedom than actuators), which is the basis for the design of vehicles such as the Segway, the self-balancing scooter, hoverboard, or self-balancing board, among others. The authors present a model for the Furuta Pendulum using the equations of Euler-Lagrange and the methodology to identify a black-box model by training an NNARMAX (Neural Network Auto-Regressive Moving Average with exogenous inputs). The results show that two interconnected MISO-NNARMAX estimates 10-step-ahead predictions accurately for the horizontal and vertical angles. |
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