特殊天气污水溶解氧浓度的自适应广义预测控制

Fuzzy Adaptive Generalized Predictive Control of Dissolved Oxygen Concentration on Wastewater Treatment in Special Weather

污水处理过程具有非线性、时变、大滞后的特点,尤其在雨天暴雨等特殊天气下,污水的入水波动会对控制器造成严重干扰;文中提出一种基于模糊神经网络模型的自适应广义预测控制算法,实现对污水处理过程中溶解氧浓度的实时控制;该算法利用反馈线性化思想实现自适应广义预测控制器的设计,在证明其李雅普诺夫稳定的同时,得到修正系统的受控自回归积分滑动平均模型参数自适应规则,动态调整模型参数使系统跟踪误差达到最小;仿真实验结果表明,该算法能够稳定、快速地控制溶解氧浓度,具有较强的抗干扰能力和鲁棒性,该控制算法特别适合用于污水处理过程的特殊天气(如雨天和暴雨天)中。

Wastewater treatment process is a nonlinear, time--varying and large time--delay system. In special weather （rainy days or rainstorm days）, the variation of sewage influent has a very serious impact on the performance of wastewater treatment process. An adaptive fuzzy generalized predictive control algorithm （AFGPC） based on neural network model to control dissolved oxygen concentration is proposed in this paper. The feedback linearization theory is applied in the design of AFGPC. The Lyapunov stability of control system is proved and the adaptive rules of the controlled auto regressive integrated moving average model are presented. The model parameters can be adjusted dy- namically to minimize the system tracking error. Numerical experimental results show that the proposed control algorithm are stable, fast and robust. The proposed control algorithm is especially suitable for rain or rainstorm weather.