基于CFNN的污水处理过程溶解氧浓度在线控制

CFNN-based online control for dissolved oxygen concentration of wastewater treatment processes

污水处理过程入水干扰严重,不确定性强,因而对溶解氧浓度进行实时控制与精确控制比较困难。为了提高溶解氧浓度的控制精度和控制器的鲁棒性,提出一种基于相关熵模糊神经网络(CFNN)的溶解氧浓度在线控制方法。首先,建立了基于跟踪误差相关熵的性能准则,以抑制较大的异常值。其次,基于在线梯度下降算法调整控制器的参数,并分析了系统的稳定性。最后,基于基准仿真1号模型(BSM1)进行实验。结果表明,提出的CFNN控制器能够实时精准地跟踪溶解氧浓度,相比其他基于均方误差准则的神经网络控制器,其具有更高的控制精度与稳定性。

Due to the frequent disturbance in flow and load,as well as the large uncertainty in the wastewater treatment processes,it is difficult to control the dissolved oxygen accurately and in real-time.To improve the accuracy and robustness of the controller,an online control method of dissolved oxygen concentration using the correntropy based fuzzy neural network(CFNN)was proposed.First,the performance index was established based on the correntropy of tracking errors to suppress large outliers in the process.Then,the parameters of controller were updated by the online gradient descent algorithm.Moreover,the stability of the control system was analyzed.Finally,the experiments were carried out based on the Benchmark Simulation Model No.1(BSM1).The results prove that the CFNN controller performs better than the mean square error based neural network controller in accuracy and model stability.