Composite Control of Dissolved Oxygen Concentration Based on Performance Coordination
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摘要: 针对常规比例积分微分 (proportion-integral-derivative, PID) 控制存在精度不高, 在线自适应差的缺点, 提出了一种在线PID-TS模糊神经网络复合控制方法.该方法利用TS模糊神经网络的自学习能力提高溶解氧的控制精度, 并通过构造的性能协调因子在线调整两者权重.将提出的控制方法应用于国际基准仿真平台.结果表明:所提方法能有效控制污水中的溶解氧参数, 与常规PID和BP (back-propagation) 神经网络控制器相比, 该方法具有更优的动态性能.Abstract: Because the conventional proportion-integral-derivative (PID) algorithm has the shortcomings of low accuracy and poor adaptability, a composite method, which includes the TS fuzzy neural network (TS-FNN) and PID controller, is proposed. This control strategy can improve the accuracy of dissolved oxygen (DO) concentration by the self-learning ability of TS-FNN. Meanwhile, the parameters of the controller can be adjusted on-line by constructing performance coordination factor. Then, this method is tested based on the international benchmark simulation platform.Resultsshow that the proposed method can achieve better dynamic performance, compared with the conventional back-propagation (BP) controller and PID controller.
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