Robust adaptive control for greenhouse climate using neural networks

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 (c) 2010 John Wiley & Sons, Ltd.