Model Reference Control by Recurrent Neural Network Built with Paraconsistent Neurons for Trajectory Tracking of a Rotary Inverted Pendulum
This investigation presents a recurrent paraconsistent neural network (RPNN), as the main element of the model reference control (MRC) strategy for the rotary inverted pendulum (RIP). The RIP characteristics, as nonlinearity, twodegree-of-freedom (2DoF) motion, and under-actuated system, make it an ideal device to apply and test the RPNN. The designed paraconsistent neural model reference controller (PNMRC) uses three RPNNs: two of them to model the arm and pendulum angles and the third one to control the system while tracking a reference trajectory. The hidden neurons of the RPNN use the paraconsistent annotated logic by 2-value annotations (PAL2v) rules as an activation function. PAL2v, as a member of the paraconsistent logics family, deals with uncertain and contradictory data, representing a potentially robust alternative to applications of artificial neural networks in control. The PAL2v neuron is detailed and compared with other activation functions in recurrent neural networks (RNN). With real-time experiments, the PNMRC strategy is compared with classical control methodology, presenting excellent performance
Year of publication: |
2023
|
---|---|
Authors: | de Carvalho Junior, Arnaldo ; Angelico, Bruno Augusto ; Justo, Joao Francisco ; de Oliveira, Alexandre Manicoba ; da Silva Filho, João Inacio |
Publisher: |
[S.l.] : SSRN |
Saved in:
freely available
Saved in favorites
Similar items by subject
-
Find similar items by using search terms and synonyms from our Thesaurus for Economics (STW).