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摘要: 针对城市污水处理过程时滞导致难以稳定控制的问题, 提出一种自适应滑模控制方法(Adaptive sliding mode control, ASMC). 首先, 分析推流时滞对城市污水处理生化反应过程的影响, 建立时滞影响下的城市污水处理运行控制模型; 其次, 设计一种基于模糊神经网络的预估补偿模型, 完成滞后变量的准确预测, 实现控制模型中变量时刻的统一; 最后, 设计一种具有自适应开关增益系数的滑模控制器(Sliding mode control, SMC), 实现溶解氧和硝态氮的稳定控制. 将提出的自适应滑模控制方法应用于城市污水处理过程基准仿真平台, 实验结果显示该方法能够实现城市污水处理运行过程稳定控制.Abstract: Aiming at the problem of time delay affecting stable control in municipal wastewater treatment processes, an adaptive sliding mode control (ASMC) method is proposed in this paper. First, the influence of push-flow time delay on biochemical reaction process of wastewater treatment processes is analyzed. Then, an operating control model of wastewater treatment processes is established. Second, an estimated compensation model, based on fuzzy neural network, is designed. Then, it can complete the accurate prediction of delay variables and realize the moment unification of variables in the control model. Finally, a sliding mode controller (SMC) with adaptive switching gain coefficient is designed. Then, it can realize stable control of dissolved oxygen and nitrate nitrogen. The proposed method is applied to the benchmark simulation model. Experimental results show it can realize stable control of wastewater treatment operating process.
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图 3 溶解氧和硝态氮控制效果 (晴天天气且设定值恒定)
Fig. 3 The control results of
$ {{S_{O,5}}}$ and$ {{S_{NO,2}}}$ (dry weather with constant settings)
图 4 溶解氧和硝态氮控制误差 (晴天天气且设定值恒定)
Fig. 4 The error results of
$ {{S_{O,5}}}$ and$ {{S_{NO,2}}}$ (dry weather with constant settings)
图 5 氧传递系数和内回流变化曲线 (晴天天气且设定值恒定)
Fig. 5 The results of
$ {{K_L}{a_5}}$ and$ {{Q_a}}$ (dry weather with constant settings)
图 6 溶解氧和硝态氮控制效果 (雨天天气且设定值恒定)
Fig. 6 The control results of
${{S_{O,5}}}$ and${{S_{NO,2}}}$ (rain weather with constant settings)
图 7 溶解氧和硝态氮控制误差 (雨天天气且设定值恒定)
Fig. 7 The error results of
${{S_{O,5}}}$ and${{S_{NO,2}}}$ (rain weather with constant settings)
图 8 氧传递系数和内回流变化曲线 (雨天天气且设定值恒定)
Fig. 8 The results of
$ {{K_L}{a_5}}$ and$ {{Q_a}}$ (rain weather with constant settings)
图 9 溶解氧和硝态氮控制效果 (晴天天气且设定值变化)
Fig. 9 The control results of
${{S_{O,5}}}$ and${{S_{NO,2}}}$ (dry weather with changing settings)
图 10 溶解氧和硝态氮控制误差 (晴天天气且设定值变化)
Fig. 10 The error results of
${{S_{O,5}}}$ and${{S_{NO,2}}}$ (dry weather with changing settings)
图 11 氧传递系数和内回流变化曲线 (晴天天气且设定值变化)
Fig. 11 The results of
$ {{K_L}{a_5}}$ and$ {{Q_a}}$ (dry weather with changing settings)表 1 不同控制器性能比较 (雨天天气且设定值恒定)
Table 1 The comparison results of different controllers (rain weather with constant settings)
控制策略 溶解氧 硝态氮 ISE IAE ${\rm{De}}{{\rm{v}}^{\max }}$ ISE IAE ${\rm{De}}{{\rm{v}}^{\max }}$ ASMC 4.930 ×$10^{-3}$ 0.035 0.490 5.270 ×$10^{-3}$ 0.046 0.420 SMC[20] 5.520 ×$10^{-3}$ 0.030 0.610 5.620 ×$10^{-3}$ 0.046 0.440 FNNC[34] 1.060 ×$10^{-2}$ 0.075 0.560 9.950 ×$10^{-3}$ 0.076 0.560 PID[12] 1.430 ×$10^{-2}$ 0.072 0.740 8.100 ×$10^{-3}$ 0.056 0.530 
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表 2 不同控制器性能比较 (晴天天气且设定值变化)
Table 2 The comparison results of different controllers (dry weather with changing settings)
控制策略 溶解氧 硝态氮 ISE IAE ${\rm{De}}{{\rm{v}}^{\max }}$ ISE IAE ${\rm{De}}{{\rm{v}}^{\max }}$ ASMC 2.120 ×$10^{-3}$ 0.030 0.340 3.310 ×$10^{-3}$ 0.043 0.330 SMC[20] 3.640 ×$10^{-3}$ 0.032 0.360 3.870 ×$10^{-3}$ 0.044 0.390 FNNC[34] 7.750 ×$10^{-3}$ 0.061 0.480 9.900 ×$10^{-3}$ 0.075 0.500 PID[12] 7.930 ×$10^{-3}$ 0.045 0.850 4.780 ×$10^{-3}$ 0.047 0.490
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