A Method of Simple Adaptive Control for Nonlinear Systems Using Neural Networks

Authors:
Muhammad Yasser;Agus Trisanto;Jianming Lu;Takashi Yahagi
Affiliations:
The authors are with the Graduate School of Science and Technology, Chiba University, Chiba-shi, 263-8522 Japan. E-mail: yasser@graduate.chiba-u.jp;The authors are with the Graduate School of Science and Technology, Chiba University, Chiba-shi, 263-8522 Japan. E-mail: yasser@graduate.chiba-u.jp;The authors are with the Graduate School of Science and Technology, Chiba University, Chiba-shi, 263-8522 Japan. E-mail: yasser@graduate.chiba-u.jp;The authors are with the Graduate School of Science and Technology, Chiba University, Chiba-shi, 263-8522 Japan. E-mail: yasser@graduate.chiba-u.jp
Venue:
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Year:
2006

Citing 0
Cited 1

Nonlinear and Neural Networks Based Adaptive Control for a Wastewater Treatment Bioprocess

KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part II

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a method of simple adaptive control (SAC) using neural networks for a class of nonlinear systems with bounded-input bounded-output (BIBO) and bounded nonlinearity. The control input is given by the sum of the output of the simple adaptive controller and the output of the neural network. The neural network is used to compensate for the nonlinearity of the plant dynamics that is not taken into consideration in the usual SAC. The role of the neural network is to construct a linearized model by minimizing the output error caused by nonlinearities in the control systems. Furthermore, convergence and stability analysis of the proposed method is performed. Finally, the effectiveness of the proposed method is confirmed through computer simulation.