Neuro-Self Tuning Adaptive Controller for Non-Linear Dynamical Systems

Authors

  • Ahmed Sabah Abdul Ameer Al-Araji University of Technology

Keywords:

Self-Tuning --- Neuro Network --- Adaptive Controller.

Abstract

In this paper, a self-tuning adaptive neural controller strategy for unknown nonlinear system is presented. The system considered is described by an unknown NARMA-L2 model and a feedforward neural network is used to learn the model with two stages. The first stage is learned off-line with two configuration serial-parallel model & parallel model to ensure that model output is equal to actual output of the system & to find the jacobain of the system. Which appears to be of critical importance parameter as it is used for the feedback controller and the second stage is learned on-line to modify the weights of the model in order to control the variable parameters that will occur to the system. A back propagation neural network is applied to learn the control structure for self-tuning PID type neuro-controller. Where the neural network is used to minimize the error function by adjusting the PID gains. Simulation results show that the self-tuning PID scheme can deal with a large unknown nonlinearity

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Published

2017-09-30

Issue

Section

Articles

How to Cite

Neuro-Self Tuning Adaptive Controller for Non-Linear Dynamical Systems. (2017). Al-Khwarizmi Engineering Journal, 1(1), 1-18. https://alkej.uobaghdad.edu.iq/index.php/alkej/article/view/16

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