<abstract xmlns="http://www.w3.org/1999/xhtml"><p>A very important problem in designing of controlling systems is to choose the right type of architecture of controller. And it is always a compromise between accuracy, difficulty in setting up, technical complexity and cost, expandability, flexibility and so on. In this paper, multipurpose adaptive controller with implementation of artificial neural network is offered as an answer to a wide range of tasks related to regulation. The effectiveness of the approach is demonstrated by the example of an adaptive thermostat. It also compares its capabilities with those of classic PID controller. The core of this approach is the use of an artificial neural network capable of predicting the behaviour of controlled object within its known range of parameters. Since such a network, being trained, is a model of a regulated system with arbitrary precision, it can be analysed to make optimal management decisions at the moment or in a number of steps. Network learning algorithm is backpropagation and its modified version is used to analyse an already trained network in order to find the optimal solution for the regulator. Software implementation, such as graphical user interface, routines related to neural network and many other, is done using Java programming language and Processing open-source integrated development environment.</p></abstract>

A very important problem in designing of controlling systems is to choose the right type of architecture of controller. And it is always a compromise between accuracy, difficulty in setting up, technical complexity and cost, expandability, flexibility and so on. In this paper, multipurpose adaptive controller with implementation of artificial neural network is offered as an answer to a wide range of tasks related to regulation. The effectiveness of the approach is demonstrated by the example of an adaptive thermostat. It also compares its capabilities with those of classic PID controller. The core of this approach is the use of an artificial neural network capable of predicting the behaviour of controlled object within its known range of parameters. Since such a network, being trained, is a model of a regulated system with arbitrary precision, it can be analysed to make optimal management decisions at the moment or in a number of steps. Network learning algorithm is backpropagation and its modified version is used to analyse an already trained network in order to find the optimal solution for the regulator. Software implementation, such as graphical user interface, routines related to neural network and many other, is done using Java programming language and Processing open-source integrated development environment.

Predictive Neural Network in Multipurpose Self-Tuning Controller

Acta Mechanica et Automatica

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Predictive Neural Network in Multipurpose Self-Tuning Controller"}

A very important problem in designing of controlling systems is to choose the right type of architecture of controller. And it is always a compromise between...

Predictive Neural Network in Multipurpose Self-Tuning Controller

Pubblicato online: 24 lug 2020

Pagine: 114 - 120

Ricevuto: 16 apr 2020

Accettato: 22 lug 2020

DOI: https://doi.org/10.2478/ama-2020-0017

Parole chiave
Artificial neural network, adaptive regulator, backpropagation algorithm, system modelling

© 2020 Oleksiy Bondar, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Predictive Neural Network in Multipurpose Self-Tuning Controller

Pubblicato online: 24 lug 2020

Pagine: 114 - 120

Ricevuto: 16 apr 2020

Accettato: 22 lug 2020

DOI: https://doi.org/10.2478/ama-2020-0017

Parole chiaveArtificial neural network, adaptive regulator, backpropagation algorithm, system modelling

© 2020 Oleksiy Bondar, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Parole chiave
Artificial neural network, adaptive regulator, backpropagation algorithm, system modelling