Use of neural networks for quick and accurate auto-tuning of PID controller

Authors:
Giulio D'Emilia;Antonio Marra;Emanuela Natale
Affiliations:
Dipartimento di Ingegneria Meccanica, Energetica e Gestionale (DIMEG), Universití degli Studi di L'Aquila, L'Aquila, Italy;ELAU Italia S.r.l., Bologna, Italy;Dipartimento di Ingegneria Meccanica, Energetica e Gestionale (DIMEG), Universití degli Studi di L'Aquila, L'Aquila, Italy
Venue:
Robotics and Computer-Integrated Manufacturing
Year:
2007

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Abstract

With reference to a real industrial application of process control, some considerations are discussed concerning the accuracy of methods for auto-tuning of proportional, integral and derivative factor (PID). In particular, a theoretical-experimental approach is described, that allows to evaluate the adequateness of new methods for auto-tuning of PID, able to significantly reduce the time duration for auto-tuning with respect to traditional ones. This result has been achieved by using suitable techniques of experimental data processing, based on neural-networks algorithms, set for this specific application. The effect on described methodology of environmental and operating disturbances is also described.