Use of neural networks for quick and accurate auto-tuning of PID controller |
| |
Authors: | Giulio DEmilia Antonio Marra Emanuela Natale |
| |
Affiliation: | aDipartimento di Ingegneria Meccanica, Energetica e Gestionale (DIMEG), Università degli Studi di L’Aquila, L’Aquila, Italy;bELAU Italia S.r.l., Bologna, Italy |
| |
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. |
| |
Keywords: | PID controller Auto-tuning Neural networks Standard deviation Measurement |
本文献已被 ScienceDirect 等数据库收录! |
|