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Discrete PID Controller Tuning Using Piecewise-Linear Neural Network
Authors: Doležel Petr | Taufer Ivan | Mareš Jan
Year: 2012
Type of publication: kapitola v odborné knize
Name of source: Introduction to PID Controllers - Theory, Tuning and Application to Frontier Areas
Publisher name: InTech
Place: Rijeka
Page from-to: 193-210
Titles:
Language Name Abstract Keywords
cze Nastavování diskrétního PID regulátoru pomocí neuronové sítě s po částech lineárními aktivačními funkcemi Ačkoliv byly PID regulátory poprvé aplikovány již na přelomu 19. a 20. století, stále se v průmyslu pro řízení procesů majoritně používají. Článek představuje novou metodu průběžného nastavování PID regulátoru pomocí po částech lineární neuronové sítě jako modelu řízené soustavy. řízení procesů; umělá neuronová síť; po částech lineární aktivační funkce
eng Discrete PID Controller Tuning Using Piecewise-Linear Neural Network PID controller (which is an acronym to “proportional, integral and derivative”) is a type of device used for process control. As first practical use of PID controller dates to 1890s (Bennett, 1993), PID controllers are spread widely in various control applications till these days. In process control today, more than 95% of the control loops are PID type. PID controllers have experienced many changes in technology, from mechanics and pneumatics to microprocessors and computers. Especially microprocessors have influenced PID controllers applying significantly. They have given possibilities to provide additional features like automatic tuning or continuous adaptation – and continuous adaptation of PID controller via neural model of controlled system (which is considered to be significantly nonlinear) is the aim of this contribution. Artificial Neural Networks have traditionally enjoyed considerable attention in process control applications, especially for their universal approximation abilities. In the contribution, there is to be explained how to use artificial neural networks with piecewise-linear activation functions in hidden layer in controller design. To be more specific, there is described technique of controlled plant linearization using nonlinear neural model. Obtained linearized model is in a shape of linear difference equation and it can be used for PID controller parameters tuning. process control; artificial neural network; piecewise-linear activation function

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