摘要：

由于复杂多变的生化反应、进水流量和浓度的不断变化，污水处理过程表现出强非线性和时变性等特征，从而导致其过程变量难以精确控制。设计一种麻雀搜索算法(SSA)优化的变论域模糊比例、积分和微分(PID)控制器，对溶解氧和硝态氮浓度进行跟踪控制。首先利用SSA优化第5单元和第2单元的变论域模糊PID控制器的PID初始参数值；然后进行二次寻优，即对量化因子和比例因子进行优化，并设计基于模糊规则的论域自适应调整策略在线整定控制器参数，以提高控制器的跟踪精度；最后应用污水处理过程国际基准仿真平台进行恒值和动态变值跟踪控制的实验验证。实验结果表明，与基于自适应伸缩因子变论域模糊PID控制器、模糊PID控制器、常规PID控制器相比，所设计控制器的绝对误差积分指标明显降低，在有效降低能耗的同时提升了出水水质。

关键词: 污水处理过程, 麻雀搜索算法, 变论域模糊, 比例、积分、微分控制器, 参数优化

Abstract:

Owing to complex biochemical reactions and the constant change in influent flow and concentration, sewage treatment processes exhibit strong nonlinear and time-varying characteristics, making it difficult to control the process variables accurately. Therefore, this paper presents a fuzzy Proportional, Integral, Derivative (PID) controller optimized by the Sparrow Search Algorithm (SSA) to track the concentration of dissolved oxygen and nitrate-nitrogen. First, SSA is used to optimize the initial PID parameters of the variable theory domain fuzzy PID controller of units 5 and 2. Then, quadratic optimization is carried out, that is, the quantization factor and scale factor are optimized. A theory domain adaptive adjustment strategy based on fuzzy rules is designed to adjust the controller parameters online, to improve the tracking accuracy of the controller. Finally, Benchmark Simulation Model no.1 (BSM1) for the sewage treatment process is used to experimentally verify constant value and dynamic variable value tracking control, and the applicability of SSA in wastewater treatment process is analyzed. Experimental results show that compared with the conventional fuzzy PID controller based on the adaptive scaling factor variable theory domain, the absolute error integral index of the designed controller is reduced. Moreover, energy consumption is reduced while effluent quality is improved.

Key words: sewage treatment process, Sparrow Search Algorithm (SSA), variable discourse domain fuzzy, Proportional, Integral, Derivative (PID) controller, parameter optimization