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Fuzzy neural network sliding mode control for long delay time systems based on fuzzy prediction

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Abstract

Delay time, which may degrade the control performance, is frequently encountered in various control processes. The fuzzy neural network sliding mode controller (FNNSMC), which incorporates the fuzzy neural network (FNN) with the sliding mode controller (SMC), is developed to control the long delay system with unknown model based on fuzzy prediction algorithm in the paper. According to the characteristics of the long delay systems, we simulate the manual operating process and predict the delayed error and its derivative based on the information of the input and output variables of the process, and then feedback these prediction values to the FNN and train the FNN with the regulation function by the idea of sliding mode control until the better control results are obtained. The FNNSMC has more robustness due to the abilities of the learning and reasoning and can eliminate the drawbacks of the general SMC, namely the chattering in the control signal and the needing knowledge of the bounds of the disturbances and uncertainties. Simulation examples demonstrate the advantages of the proposed control scheme.

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Authors and Affiliations

  1. Institute of Automation, Southeast University, Nanjing, 210096, People’s Republic of China

    Feipeng Da

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  1. Feipeng Da
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Correspondence to Feipeng Da.

Additional information

This work was supported by 973 program of China (No. 2002CB312200) and BK2003405.

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Da, F. Fuzzy neural network sliding mode control for long delay time systems based on fuzzy prediction. Neural Comput & Applic 17, 531–539 (2008). https://doi.org/10.1007/s00521-007-0130-x

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  • DOI: https://doi.org/10.1007/s00521-007-0130-x

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