EKF Based Generalized Predictive Control of Nonlinear Systems

Yıl 2016, Special Issue (2016), 148 - 154, 01.12.2016

Erdem Dilmen , Selami Beyhan

https://doi.org/10.18100/ijamec.268866

Öz

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.

Anahtar Kelimeler

Generalized predictive control, ARX, dynamic neural network, Kalman filter and extended Kalman filter, nonlinear systems and adaptive learning rate

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