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PID, Fuzzy and Model Predictive Control Applied to a Practical Nonlinear Plant

PID, Fuzzy and Model Predictive Control Applied to a Practical Nonlinear Plant

Kai Borgeest, Peter Josef Schneider
Copyright: © 2016 |Volume: 4 |Issue: 1 |Pages: 24
ISSN: 2166-7195|EISSN: 2166-7209|EISBN13: 9781466693760|DOI: 10.4018/IJRAT.2016010102
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MLA

Borgeest, Kai, and Peter Josef Schneider. "PID, Fuzzy and Model Predictive Control Applied to a Practical Nonlinear Plant." IJRAT vol.4, no.1 2016: pp.19-42. http://doi.org/10.4018/IJRAT.2016010102

APA

Borgeest, K. & Schneider, P. J. (2016). PID, Fuzzy and Model Predictive Control Applied to a Practical Nonlinear Plant. International Journal of Robotics Applications and Technologies (IJRAT), 4(1), 19-42. http://doi.org/10.4018/IJRAT.2016010102

Chicago

Borgeest, Kai, and Peter Josef Schneider. "PID, Fuzzy and Model Predictive Control Applied to a Practical Nonlinear Plant," International Journal of Robotics Applications and Technologies (IJRAT) 4, no.1: 19-42. http://doi.org/10.4018/IJRAT.2016010102

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Abstract

For the cooling system of a mobile machine with m control variables and with n=m correction variables different control strategies have been investigated in order to minimize power to save energy and to reduce fan noise with sufficient cooling. The plant is nonlinear and not identified. Three different kinds of controllers have been investigated in several variations, i.e. fuzzy control, PI(D) and model predictive control (MPC). 14 different criteria have been used for evaluation. In many respects a linear controller with fuzzy prediction proved best, in particular the prediction model can handle nonlinear properties of the plant. A problem of advanced control schemes with unidentified plants is the difficulty to prove stability.

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