Abstract
System states are often required to solve control-theory problems. Unfortunately, there are situations in which some or even all variables are not available from measurements, requiring them to be estimated a priori. Here, we used the paraconsistent annotated logic by 2-value annotations (PAL2v) to investigate the state estimation problem applied in the control of a Furuta pendulum. The PAL2v code blocks, labeled as paraconsistent artificial neural cells (PANC), allow designing models to handle contradictions and ambiguities. This study proposes to build estimators with a specific PAL2v cell, labeled PANC of learning by contradiction extraction (\(\mathrm {PANCL_{CTX}}\)), which derives or asymptotically integrates values depending on the selected output. Through \(\mathrm {PANCL_{CTX}}\), we built PAL2v estimators and filters, requiring low complexity mathematics, with good results when compared to standard methods.
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Carvalho, A., Justo, J.F., Angélico, B.A. et al. Paraconsistent State Estimator for a Furuta Pendulum Control. SN COMPUT. SCI. 4, 29 (2023). https://doi.org/10.1007/s42979-022-01427-z
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DOI: https://doi.org/10.1007/s42979-022-01427-z