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Neural compensator for PI soil moisture control

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Abstract

The spatial and temporal variability of a cultivated soil, with technified irrigation systems, requires adaptive control systems to the varying conditions of the water–soil–crop intersystem. Therefore, an adaptive control based on a Radial Basis Function Neural Network (RBF-NN) is proposed in this paper. A static Proportional-Integral (PI) controller was tuned without modifying its parameters by adding a compensation based on RBF-NNs. In this way, the dynamic variation is approximated in real time by means of a RBF-NN. The controller is tested in simulation from a model of water distribution in the soil with extraction by a crop. The results obtained with this method are compared with a traditional Proportional-Integral-Derivative (PID) controller. The comparisons are made taking into account compromise between the amount of water applied and irrigation frequency to keep soil moisture values within the allowed limits. Water savings of 20% and a reduced valve activations 2 times less than the traditional PID were achieved. Finally, the behavior of the controller in the event of disturbances was evaluated, verifying the rejection it produces in the face of these eventualities.

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Data availibility

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

This work has been carried out thanks to the support of the Universidad Nacional de San Juan and the Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) of Argentina.

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Author notes

  1. Francisco Rossomando, Flavio Capraro and Carlos Soria have contributed equally to this work.

Authors and Affiliations

  1. Instituto de Automática (INAUT), Universidad Nacional de San Juan - Consejo Nacional de Investigaciones Científicas y Técnicas, Av. San Martín (Oeste) 1109, J5400ARL, San Juan, Argentina

    Juan A. Gomez, Francisco Rossomando, Flavio Capraro & Carlos Soria

Authors
  1. Juan A. Gomez
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  2. Francisco Rossomando
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  3. Flavio Capraro
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  4. Carlos Soria
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Correspondence to Juan A. Gomez.

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Gomez, J.A., Rossomando, F., Capraro, F. et al. Neural compensator for PI soil moisture control. Neural Comput & Applic 35, 19131–19144 (2023). https://doi.org/10.1007/s00521-023-08723-6

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