GMDH Neural Network-Based Enhanced Data-Driven Adaptive Control Design for Unknown Nonlinear Systems in the Presence of Quantized Data.

Document Type : Original Article

Authors

1 Department of Electrical Engineering, Faculty of Engineering, Shahid Bahonar University of Kerman, Kerman, Iran

2 Department of Electrical Engineering, Shahid Bahonar University of Kerman

3 دانشکده فنی و مهندسی شهید باهنر کرمان، گروه برق

Abstract

This paper proposes an Enhanced Data-Driven Quantized Model-Free Adaptive Control (EDD-QMFAC) structure for a class of unknown nonlinear systems based on the Group Method of Data Handling (GMDH) neural network. In this study, the output quantized data is given to the GMDH block to overcome the data quantization challenges in Data-Driven Control methods. In the proposed control loop the GMDH derives a model to estimate the actual output of the system from the quantized output data based on the predictive feature of this network. The controller then generates the input control signal based on the system’s estimated actual output data. The Lyapunov theory is used to prove the stability of the suggested structure. The simulation results demonstrate the advantages of the proposed control structure over the conventional QMFAC.

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TABRIZ JOURNAL OF ELECTRICAL ENGINEERING

Articles in Press, Corrected Proof
Available Online from 03 July 2024

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