Robust adaptive control for greenhouse climate using neural networks |
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Authors: | Xiaoli Luan Peng Shi Fei Liu |
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Affiliation: | 1. Key Laboratory of Advanced Control for Light Industry Processes, Ministry of Education, Institute of Automation, Jiangnan University, Wuxi 214122, People's Republic of China;2. Department of Computing and Mathematical Sciences, University of Glamorgan, Pontypridd CF37 1DL, U.K.;3. School of Engineering and Science, Victoria University, Melbourne, 8001 Vic., Australia |
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Abstract: | This paper presents a general framework for robust adaptive neural network (NN)‐based feedback linearization controller design for greenhouse climate system. The controller is based on the well‐known feedback linearization, combined with radial basis functions NNs, which allows the feedback linearization technique to be used in an adaptive way. In addition, a robust sliding mode control is incorporated to deal with the bounded disturbances and the approximation errors of NNs. As a result, an inherently nonlinear robust adaptive control law is obtained, which not only provides fast and accurate tracking of varying set‐points, but also guarantees asymptotic tracking even if there are inherent approximation errors. Copyright © 2010 John Wiley & Sons, Ltd. |
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Keywords: | greenhouse climate control adaptive control feedback linearization neural networks |
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