TY  - JOUR
TT  - EKF Based Generalized Predictive Control of Nonlinear Systems
AU  - Dilmen, Erdem
AU  - Beyhan, Selami
PY  - 2016
DA  - December
DO  - 10.18100/ijamec.268866
JF  - International Journal of Applied Mathematics Electronics and Computers
PB  - PLUSBASE AKADEMİ ORGANİZASYON VE DANIŞMANLIK
WT  - DergiPark
SN  - 2147-8228
SP  - 148
EP  - 154
IS  - Special Issue-1
KW  - Generalized predictive control
KW  - ARX
KW  - dynamic neural network
KW  - Kalman filter and extended Kalman filter
KW  - nonlinear systems and adaptive learning rate
N2  - In this paper, Autoregressive with exogenous input (ARX) and dynamicneural network (DNN) based generalized predictive control (GPC) methods aredesigned to control of nonlinear systems. ARX and DNN models adaptivelyapproximate the plant dynamics and predict the future behavior of the nonlinearsystem. While control process goes on, the poles of the ARX and DNN models areconstrained in a stable region using a projection operator for structuralstability. Simulation results are given to compare the tracking performances ofthe methods. ARX-GPC and DNN-GPC both yield good tracking performances whilekeeping the changes in control signal as low as possible. The simulationresults show that even though ARX is a linear model, it provides acceptabletracking results as well as DNN model.
CR  - [1]	K.J. Astrom.  Theory and applications of adaptive control-a survey.   Automatica, 19(5):471 – 486, 1983.
CR  - [2]	Selami Beyhan and Musa Alc.  Extended fuzzy function model with stable learning methods for online system identification.   International Journal of Adaptive Control and Signal Processing, 25(2):168–182, 2011.
CR  - [3]	D.W. Clarke, C. Mohtadi, and P.S. Tuffs.  Generalized predictive control part i. the basic algorithm.   Automatica, 23(2):137 – 148, 1987.
CR  - [4]	D W Clarke, C Mohtadi, and P S Tuffs.  Generalized predictive control part ii. extensions and interpretations.   Automatica, 23(2):149–160, March 1987.
CR  - [5]	Kaynak O Efe M O, Abadoglu E.  A novel analysis and design of a neural network assisted nonlinear controller for a bioreactor.   International Journal of Robust and Nonlinear Control, 9:799–815, 1999.
CR  - [6]	M. Ghiassi, H. Saidane, and D.K. Zimbra.  A dynamic artificial neural network model for forecasting time series events.   International Journal of Forecasting, 21(2):341 – 362, 2005.
CR  - [7]	Sanqing Hu and Jun Wang.  Global stability of a class of discrete-time recurrent neural networks.   Circuits and Systems I: Fundamental Theory and Applications, IEEE Transactions on, 49(8):1104–1117, Aug 2002.
CR  - [8]	Petros A. Ioannou and Jing Sun.   Robust Adaptive Control.  Prentice-Hall, Inc., Upper Saddle River, NJ, USA, 1995.
CR  - [9]	Serdar Iplikci.  A support vector machine based control application to the experimental three-tank system. ISA Transactions, 49(3):376 – 386, 2010.
CR  - [10]	Liang Jin, Peter N. Nikiforuk, and Madan M. Gupta.  Absolute stability conditions for discrete-time recurrent neural networks.   IEEE Transactions on Neural Networks, 5(6):954–964, 1994.
UR  - https://doi.org/10.18100/ijamec.268866
L1  - https://dergipark.org.tr/en/download/article-file/235532
ER  -