基于LM算法的溶解氧神经网络预测控制

Neural Network Predictive Control for Dissolved Oxygen Based on Levenberg-Marquardt Algorithm

针对污水处理溶解氧时变、非线性以及设定值难以跟踪控制的问题,提出了一种基于Levenberg-Marquardt算法(LM算法)的溶解氧浓度神经网络预测控制器的设计方法。首先在国际水协会提出的活性污泥1号模型(ASM1)基础上,经过合理的假设和约束,得到简化的溶解氧浓度模型,经过BP神经网络系统辨识和模型预测设计了溶解氧神经网络预测控制器。并采用LM算法改进了BP神经网络,克服了容易陷入局部极小值、收敛速度慢的缺点,提高了神经网络预测精度。仿真结果表明,神经网络预测控制具有很好的自适应性和鲁棒性,提高了溶解氧跟踪控制性能。

The dissolved oxygen（ DO） concentration is of great importance to wastewater treatment due to its influence on effluent quality and operational costs. However,the DO concentration is difficult to be controlled owing to the time-varying and nonlinear characteristics. Considering these issues,a neural network predictive controller（ NNPC） based on Levenberg-Marquardt（ LM） algorithm was proposed.Firstly,a simplified DO model was established after reasonable hypotheses and constrains in terms of activated sludge model No. 1（ ASM1） proposed by International Water Association（ IWA）. Then the NNPC was applied to the simplified DO model through system identification with BP neural network and model prediction. Furthermore,the LM algorithm integrated the advantages of the gradient steepest descent and Newton methods was used to improve the general BP neural network,which overcame the drawbacks of falling into local minimum easily and slow convergence speed. The simulation results indicated that the improved neural network had good performance in system identification with error less than 3%. Compared with conventional PID control and model predictive control（ MPC）,the NNPC achieved smoother and better tracking performance and brought obvious improvement. Finally,two measured disturbances were added and good adaptability and robustness were obtained by NNPC. In this way,this method not only can achieve the standard of effluent water quality,but also can reduce the energy consumption of aeration significantly.