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Peer-Reviewed Academic JournalInternational Journal of Applied Methods in Electronics and Computers
ISSN: 3023-4409DOI Prefix: 10.58190/ijamec
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Pages: 148-154

EKF Based Generalized Predictive Control of Nonlinear Systems

Erdem Dilmen •
Selami Beyhan
Publication DateDecember 1, 2016
Volume / IssueVol. , No. (pp. 148-154)
subject

Abstract

In this paper, Autoregressive with exogenous input (ARX) and dynamic neural network (DNN) based generalized predictive control (GPC) methods are designed to control of nonlinear systems. ARX and DNN models adaptively approximate the plant dynamics and predict the future behavior of the nonlinear system. While control process goes on, the poles of the ARX and DNN models are constrained in a stable region using a projection operator for structural stability. Simulation results are given to compare the tracking performances of the methods. ARX-GPC and DNN-GPC both yield good tracking performances while keeping the changes in control signal as low as possible. The simulation results show that even though ARX is a linear model, it provides acceptable tracking results as well as DNN model.
Keywords:Generalized predictive controlARXdynamic neural networkKalman filter and extended Kalman filternonlinear systems and adaptive learning rate
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How to Cite This Article

E. D., S. B. (2016). EKF Based Generalized Predictive Control of Nonlinear Systems. International Journal of Applied Methods in Electronics and Computers, 148-154. https://doi.org/10.18100/ijamec.268866