Artificial Neural Networks Models Based on ARX and State Space Forms and Adaptive Control PID/LQR of Systems Based on Peltier Cells

Denis Fabricio Sousa De Sá; João Viana Fonseca Neto

doi:10.31686/ijier.vol9.iss11.3540

Authors

Denis Fabricio Sousa De Sá Federal University of Maranhão https://orcid.org/0000-0002-5694-7901
João Viana Fonseca Neto Federal University of Maranhão

DOI:

https://doi.org/10.31686/ijier.vol9.iss11.3540

Keywords:

Adaptive Control, Neural Networks, System Identification, Thermal System, ltier Cell actuator, Control Design, LQR

Abstract

To improve the performance of a thermal plant based on Peltier cell actuators, an online parametric estimation via artificial neural networks and adaptive controller is presented. The control actions are based on adaptive digital controller and an adaptive quadratic linear regulator approaches. The Artificial neural networks topology is based on ARX and NARX models, and its training algorithms, such as accelerated backpropagation and recursive least square. The Control strategies are design-oriented to adaptive digital PID controller and quadratic linear regulator framework. The proposal is evaluated on temperature control of an object that is inside of a chamber.

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Author Biographies

Denis Fabricio Sousa De Sá, Federal University of Maranhão

Department of Electrical Engineering-Balsas
João Viana Fonseca Neto, Federal University of Maranhão

Professor, Department of Electrical Engineering and PPGE

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