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PID Tuning with Neural Networks

  • Antonio Marino
  • Filippo NeriEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11431)

Abstract

In this work we will report our initial investigation of how a neural network architecture could become an efficient tool to model Proportional-Integral-Derivative controller (PID controller). It is well known that neural networks are excellent function approximators, we will then be investigating if a recursive neural networks could be suitable to model and tune PID controllers thus could assist in determining the controller’s proportional, integral, and the derivative gains. A preliminary evaluation is reported.

Keywords

PID tuning and approximation Machine learning Neural networks 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Electrical Engineering and Information TechnologiesUniversity of NaplesNaplesItaly

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