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Fuzzy super-twisting sliding mode control for municipal wastewater nitrification process

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Abstract

A fuzzy super-twisting algorithm sliding mode controller is developed for the dissolved oxygen concentration in municipal wastewater nitrification process. First, a fuzzy neural network (FNN) model is designed to approach the oxygen dynamics with unmeasurable disturbances, then the established model consists of the nominal system model and the modelling error. Second, based on the FNN model, a super-twisting sliding mode controller is employed to stabilize the nominal system and to suppress the modelling error. Moreover, the stability of the system is investigated and an adaption law is applied to ensure the robustness of the closed-loop system. Finally, the comparison experiments on benchmark simulation model no. 2 (BSM2) of wastewater treatment show the advantages of the proposed method in multiple-units oxygen concentration control.

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Authors and Affiliations

  1. Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China

    HongGui Han, Tong Wang, HaoYuan Sun, XiaoLong Wu & JunFei Qiao

  2. School of Mechanical and Materials Engineering, North China University of Technology, Beijing, 100041, China

    Wen Li

Authors
  1. HongGui Han
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  2. Tong Wang
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  3. HaoYuan Sun
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  4. XiaoLong Wu
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  5. Wen Li
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  6. JunFei Qiao
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Corresponding author

Correspondence to HongGui Han.

Additional information

This work was supported by the National Nutural Science Foundation of China (Grant Nos. 61890930-5, 61903010, 62021003 and 62125301), the National Key Research and Development Project (Grant No. 2018YFC1900800-5), Beijing Outstanding Young Scientist Program (Grant No. BJJWZYJH01201910005020), Beijing Natural Science Foundation (Grant No. KZ202110005009), CAAI-Huawei MindSpore Open Fund (Grant No. CAAIXSJLJJ-2021-017A), and Beijing Postdoctoral Research Foundation.

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Han, H., Wang, T., Sun, H. et al. Fuzzy super-twisting sliding mode control for municipal wastewater nitrification process. Sci. China Technol. Sci. 65, 2420–2428 (2022). https://doi.org/10.1007/s11431-021-2050-x

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  • DOI: https://doi.org/10.1007/s11431-021-2050-x

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