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Self-Learning Fuzzy Sliding-Mode Control for a Water Bath Temperature Control System

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A self-learning fuzzy sliding-mode controller (SLFSMC) is proposed to control the temperature of a water bath. The SLFSMC system automatically tunes the rule bases using a rule modifier and the updating value of each rule is based on the fuzzy firing weight. In addition, this controller can be used for on-line learning in real-time control systems. In order to illustrate the performance of the proposed control method, it is compared with a proportional derivative-type fuzzy control (PDFC) and a gain-tuning fuzzy control (GTFC). These three algorithms are applied to a water bath temperature control and are simulated under the same conditions. The effect of load disturbance, the response to control, the tracking performance, and suitable sampling time are determined for each system. The simulation results show that the SLFSMC has superior characteristics, is more simple to use and has a fast response, so the SLFSMC performs better than the PDFC and GTFC.

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This work was supported partially by the National Science Council of the Republic of China under Grant NSC 98-2221-E-155-058-MY3

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Correspondence to Chih-Min Lin.

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Boldbaatar, EA., Lin, CM. Self-Learning Fuzzy Sliding-Mode Control for a Water Bath Temperature Control System. Int. J. Fuzzy Syst. 17, 31–38 (2015).

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