基于神经网络逆模型的污水pH值内模控制策略

Internal model control strategy for pH value of sewage based on neural network inverse model

针对污水处理中和反应过程pH值控制具有强干扰和模型参数易变等特点,利用内模控制方法的设定值响应和干扰响应之间相互独立的优点,提出一种基于内模控制和神经网络逆模型相结合的pH值优化控制策略。通过在系统中插入低通滤波器,并采用RBF神经网络在线辨识被控对象的逆模型,提高污水处理pH值控制的鲁棒性和抗干扰能力,有效解决中和反应pH值控制过程中模型参数易变的问题。MATLAB仿真结果表明:与常规PID控制和不带滤波器的神经内模控制策略相比,提出的优化控制策略超调量最多降低17.4%,调节时间最多减少113.6 s,有效提高了系统鲁棒性和抗干扰能力。工程应用表明:使用所提策略后,pH值控制偏差在±0.2以内,系统的控制精度和稳定性显著提高。

Given the significant interference and the variable model parameters encountered in pH value control in the sewage treatment reaction process,this study capitalizes on the independence between the set value response and the interference response of the internal model control to proposed a pH optimization control strategy,integrating internal model control and a neural network inverse model.By incorporating a low-pass filter into the system and using the RBF neural network for online identification of the inverse model of the controlled object,the robustness and anti-interference capability of pH value control in the sewage treatment are improved.This approach effectively addresses the challenge of varying model parameters in the neutralization reaction pH value control process.MATLAB simulation results show that compared with conventional PID control and neural internal model control strategies without a filter,the proposed optimal control strategy reduces overshoot by up to 17.4%and shortens the adjustment time by up to 113.6 s.These improvements effectively improve the system’s robustness and anti-interference capabilities.Engineering applications validate the effectiveness of the proposed strategy,ensuring pH value control deviation within±0.2.Consequently,the control accuracy and system stability are significantly improved.