## 1 Introduction  
In China, the use of traditional methods to irrigate crops is prone to the problem of water abuse and China's fertilizer use rate ranks first in the world all the year round, but the utilization rate of chemical fertilizers is not high. The integrated water and fertilizer technology is a new technology that combines irrigation and fertilization. According to the nutritional needs of crops, the pH value of liquid fertilizers is precisely regulated so that the roots of crops can fully absorb nutrients [1]. This technology can effectively reduce the pollution of fertilizers to soil and groundwater, and protect the ecological environment [2]. However, there is often a time lag in the process of pH adjustment, and the change in pH value of liquid fertilizer is also nonlinear. Therefore, how to quickly and accurately adjust the pH value of fertilizer to an appropriate range in the process of fertilization is the key research field of water fertilizer integration technology [3]. [PERSON] et al. [4] proposed an adaptive PI algorithm that uses a simple process model to predict the pH closed-loop response and its sensitivity to PI parameter settings, and finally, the obtained information was directly used to adjust the PI controller parameters on-line. [PERSON] et al. [5] proposed an adaptive control scheme based on Recurrent Neural Network (RNN). The scheme included an on-line adaptive RNN estimator and RNN controller, and the estimator weights were updated recursively by back-propagation algorithm. The controller weights were corrected by the steepest descent method. The proposed scheme was compared with the model-based IMC controller, and the results showed that the RNN-based controller had better performance in the nonlinear pH neutralization process. [PERSON] et al. [6] used a real-time adaptive algorithm based on the Extended Kalman Filter (EKF) to correct the artificial neural network predictions at process runtime, which reduced the sum of squared errors in pH by 64.3% compared to the MPC of the artificial neural network without model adaptation. [PERSON] and [PERSON] [7] developed a Continuous Stirred-Tank Reactor (CSTR) neutralization simulator and an adaptive Particle Swarm Optimization (PSO) algorithm for automatic selection of Reinforcement Learning hyperparameters. During regulation and servo operation, the controller stabilized the effluent pH in the neutral range better than the PID controller. [PERSON] et al. [8] proposed an adaptive control scheme for pH value based on a fuzzy logic system and verified the effectiveness of the controller through simulation and experimental research. The results showed that the controller performed well in setpoint tracking and was much better than the PI controller. [PERSON] et al. [9] proposed a predictive control method based on Decentralized Fuzzy Inference (DFIPC), which locally linearized the nonlinear object model and predicted the future output of the control object according to its step response model. The method was applied to the pH neutralization process. The results showed that the method had better robustness than traditional model predictive control. In the industrial field, Smith prediction compensation is mainly used to solve the pure lag of the system [10]. [PERSON] [11] proposed a Smith predictor combined with Linear Active Disturbance Rejection Control (LADRC) and analyzed the stability of the Smith + LADRC time-delay control system from a theoretical point of view. Simulation and experimental results showed that the algorithm was superior to the traditional method in terms of overshoot and response time. [PERSON] et al. [12] combined the classical Smith predictor and the adaptive Smith predictor in a networked control system and compared it with other delay compensation schemes. The results showed that the scheme significantly improved the performance of the networked control system and reduced the impact of delay on the system. [PERSON] et al. [13] proposed a hybrid Smith predictor and phase lead compensation method.