## 1 Introduction
AFC is a supervised data-driven control method designed with a smooth switching scheme between supervisory and nonsupervisory modes. Results show that it can learn and improve control rules resulting in accurate DO control. [PERSON] et al. simulated intelligent control method and traditional PID control method in combination. Based on their respective advantages, they achieved better control effect when they used the intelligent PID control algorithm into applications of control practice in Haicheng sewage treatment plant, China [12]. Scholars also introduced the neural network into the control of DO in wastewater treatment process, for example, back propagation (BP) neural network [13]. Furthermore, neural network is employed into some control strategies for the wastewater treatment process control. [PERSON] [14] andMirghasemi [15] proposed a robust adaptive neural network control strategy and used it to control the dissolved oxygen in activated sludge process application. The proposed method prevented weight drift and associated bursting, without sacrificing performance. They improved the control performance by using the algorithm, Cerebellar Model Arithmetic Computer (CMAC) to estimate the nonlinear behavior of the system. Results showed that it can effectively avoid state error. [PERSON] et al. proposed an on-line hybrid intelligent control system based on a genetic algorithm (GA) evolving fuzzy wavelet neural network software sensor to control dissolved oxygen (DO) in an anaerobic/anoxic/oxic (AAO) process for treating papermaking wastewater [16]. The results indicate that the reasonable forecasting and control performances were achieved with optimal DO, and the effluent quality was stable at and below the desired values in real time. It can be an effective control method, attaining not only adequate effluent quality but also minimizing the demand for energy, and is easily integrated into a global monitoring system for purposes of cost management [16]. [PERSON] et al. proposed a control method based on self-organizing T-S fuzzy neural network (SO-TSFNN), while using its powerful self-learning, fault-tolerant and adaptive abilities of the environment [17]. It realized the real-time control of dissolved oxygen of the BSM1 and achieved better control effect for DO concentration with good adaptability. [PERSON] et al. proposed a neural network predictive control method for dissolved oxygen based on Levenberg-Marquardt (LM) algorithm [18]. It overcomes the shortages of the BP neural network by combining with the LM algorithm to improve the prediction accuracy of neural network and the tracking performance of dissolved oxygen control. [PERSON] et al. proposed a new control strategy of DO concentration based on fuzzy neural network (FNN). The minimum error of the gradient descent method is used to adjust the parameters of the neural network on-line. Simulation results show that the FNN controller is better than other compared methods [19]. [PERSON] and [PERSON] studied the design approach of a neural adaptive control method based on a disturbance observer. A RBF neural network is employed to approximate the uncertain dynamic model of the wastewater treatment process. The effectiveness of the controller is verified by simulation their study [20]. [PERSON] et al. proposed a self-organizing RBF neural network model predictive control (SORBF-MPC) method for controlling DO concentration in WWTP. The hidden nodes in RBF neural network can be added or deleted online on the basis of node activity and mutual information to achieve necessary dynamics of the network. The application results of DO concentration control show that SORBF-MPC can effectively control the process of dissolved oxygen [21]. [PERSON] proposed a self-organizing fuzzy neural network (SOFNN) control method based on. According to the activation strength and mutual information, the algorithm dynamically adds and reduces the number of neurons in the regular layer to meet the dynamic changes of the actual working conditions.