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B‐splines neural network assisted PID autotuning
Author(s) -
Ruano António E.,
Azevedo Ana B.
Publication year - 1999
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/(sici)1099-1115(199906)13:4<291::aid-acs548>3.0.co;2-e
Subject(s) - pid controller , control theory (sociology) , artificial neural network , approximation error , computer science , minification , controller (irrigation) , control engineering , control (management) , engineering , artificial intelligence , algorithm , temperature control , agronomy , biology , programming language
This paper describes an extension of previous work on the subject of neural network proportional, integral and derivative (PID) autotuning. Basically, neural networks are employed to supply the three PID parameters, according to the integral of time multiplied by the absolute error (ITAE) criterion, to a standard PID controller. These networks were previously trained off‐line, remaining fixed thereafter. In order to make this approach adaptive, one additional neural network is used here to model the relation between the PID parameters and the plant identification measures to the ITAE value. This model will be afterwards employed in an on‐line minimization routine which finds the optimal PID parameters; these will be used to adapt, on‐line, the neural networks responsible for the PID parameters. Copyright © 1999 John Wiley & Sons, Ltd.