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作者:

Feng, Chengcheng (Feng, Chengcheng.) | Sun, Haoyuan (Sun, Haoyuan.) | Han, Honggui (Han, Honggui.) | Cheng, Zheng (Cheng, Zheng.) | Li, Fangyu (Li, Fangyu.)

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EI Scopus

摘要:

Wastewater treatment processes (WWTPs) are complex industrial processes with disturbance and strong nonlinearity, and it is difficult to accurately track the pre-designed dissolved oxygen concentration (DOC) in a finite time. To solve these problems, a robust dynamic surface control strategy with fixed time observer (DSRC-FTO) is proposed to achieve a stable control performance for DOC in this paper. The contributions of DSRC-FTO are three folds. First, an adaptive interval type-2 fuzzy neural network based on predictor (P-AIT2FNN) is applied to adaptively imitate the strong nonlinearity of WWTPs. Subsequently, a weight adaptive law is formulated using the predictor error to minimize modeling errors. Second, a fixed time observer (FTO), based on dynamic surface technique, is developed to actively suppress the unknown disturbances. Third, it is proved that the designed FTO can converge in fixed time. Then, the finite time stability of DSRC-FTO can be proved. Finally, DSRC-FTO is tested on the benchmark simulation model no. 1 (BSM1). Compared other existing methods, DSRC-FTO can realize accurate control for DOC in a finite time. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词:

Control nonlinearities Benchmarking Fuzzy inference Wastewater treatment Robust control Fuzzy neural networks Robustness (control systems)

作者机构:

  • [ 1 ] [Feng, Chengcheng]Beijing University of Technology, Faculty of Information Technology, Beijing; 100124, China
  • [ 2 ] [Sun, Haoyuan]Beijing University of Technology, Faculty of Information Technology, Beijing; 100124, China
  • [ 3 ] [Han, Honggui]Beijing University of Technology, Faculty of Information Technology, Beijing; 100124, China
  • [ 4 ] [Cheng, Zheng]Beijing University of Technology, Faculty of Information Technology, Beijing; 100124, China
  • [ 5 ] [Li, Fangyu]Beijing University of Technology, Faculty of Information Technology, Beijing; 100124, China

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ISSN: 1934-1768

年份: 2024

页码: 2227-2232

语种: 英文

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