Research Article | OPEN ACCESS
Simple Adaptive Neural Network Controller Design for Modern Agricultural Mechanical Systems
1Hui HU, 2Wang Yingjun, 3Xilong Qu and 4Zhongxiao Hao
1Depertment of Electrical and Information Engineering, Hunan Institute of Engineering,
Hunan Xiangtan, China
2School of Information and Engineering, Henan Institute of Science and Technology, Xinxian, China
3Department of Computer Science, Hunan Institute of Engineering, Hunan Xiangtan, China
4School of Information Science and Electrical Engineering, Hebei University of Engineering, Handan, China
Advance Journal of Food Science and Technology 2014 6:737-742
Received: February 14, 2014 | Accepted: April 22, 2014 | Published: June 10, 2014
Abstract
The study proposes a new simple output feedback adaptive tracking control scheme using neural network for a class of complicated modern agricultural mechanical systems that only the system output variables can be measured. The scheme avoids design state observer and Lipschiz assumption, SPR conditions are not required and few parameters in control laws and weights update laws need to be tuned. Only one RBF neural network is employed to approximate the lumped uncertain nonlinear function. The stability analysis of the closed-loop system is performing using a Lyapunov approach which shows that the output tracking error and all states in the closed-loop system are boundedness. The effectiveness of the proposed adaptive control scheme is demonstrated through the simulations.
Keywords:
Adaptive, agricultural mechanical systems, neural network, output feedback,
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Competing interests
The authors have no competing interests.
Open Access Policy
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Copyright
The authors have no competing interests.
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ISSN (Online): 2042-4876
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