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This paper proposes an adaptive gain fuzzy sliding mode control (AGFSMC) scheme for the synchronization of two nonlinear chaotic gyros in the presence of model uncertainties and external disturbances. In the AGFSMC scheme, the hitting controller that drives the system to the sliding surface is constructed by a set of fuzzy rules. In the proposed method, the gain of the reaching controller is adaptively adjusted to provide robustness against bounded uncertainties and external disturbances. The AGFSMC scheme can provide robustness in the absence of any knowledge about the bounds of uncertainties and external disturbances. We show that the adaptive gain scheme used in AGFSMC, improves the performance in comparison with the same control methodology that uses a fixed gain. Theoretical analysis of the AGFSMC scheme based on Lyapunov stability theory is provided. Numerical simulation on the application of the proposed method for the synchronization of two chaotic gyros is provided to demonstrate the feasibility of the method.

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This paper highlights the use of fuzzy logic to model and predict the experimental results of heat transfer in an air cooled heat exchanger equipped with the butterfly inserts. Experiments included Reynolds number ranging from 4021 to 16118 and the inclined angle of the butterfly inserts from 45• to 90•. Experimental results showed that, the maximum heat transfer by the use of butterfly insert was obtained with the inclined angle of 90•. A fuzzy inference system named Mamdani was used to expect the output membership functions to be fuzzy sets. It has been also shown that, fuzzy logic is a powerful instrument for predicting the experiments due to its low error. The average error of fuzzy with respect to experimental data was found to be 0.41% for this study.

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