Application of the Modified Fuzzy-PID-Smith Predictive Compensation Algorithm in a pH-Controlled Liquid Fertilizer System

Abstract

An appropriate pH value of liquid fertilizer can enable crops to better absorb nutrients from fertilizers. However, the mixed liquid fertilizer with high concentration of liquid fertilizer and irrigation water has a high pH value, which affects the absorption of nutrients by crops. Therefore, the precise regulation of liquid fertilizer pH value is an important link to realize the integration of water and fertilizer in modern agriculture. Due to pipeline transportation and diffusion of the regulating liquid and liquid fertilizer, the pH value control system has the characteristics of time-varying, non-linear and time-delayed models, and it is difficult for ordinary controllers to accurately control the pH value of liquid fertilizer. Therefore, modern agriculture urgently needs a controller that can adapt to non-linear and uncertain systems. According to the characteristics of the pH regulation process of liquid fertilizer, this study proposes and designs a modified fuzzy-PID-Smith predictive compensation algorithm, which adds the fuzzy-PID algorithm to the predictor of the conventional Smith algorithm to compensate for the error between the actual and theoretical models in order to reduce the decline of control quality caused by the model mismatch to the control system. To verify the practicability and robustness of the algorithm in practical applications, a liquid fertilizer pH value control system with STM32F103ZET6 as the control core was developed. The pH control system with fuzzy-PID and Smith algorithm as controller was used as the control group. The model was simulated and tested under two conditions of exact matching and imprecise matching, and performance tests were carried out under different output flow rates. The results showed that the maximum overshoot of the modified fuzzy-PID-Smith predictive compensation algorithm was significantly less than that of the other two algorithms at different output flow rates, with an average of 0.23%. The average steady-state time of adjusting the pH value of liquid fertilizer from 7.3 to 6.8 was 72 s, which was superior to the 145 s and 3.2% of fuzzy-PID and 130 s and 1.4% of the Smith controller.

Temperature control technology based on Smith fuzzy PID

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Nonlinear Adaptive Generalized Predictive Control for PH Model of Nutrient Solution in Plant Factory Based on ANFIS

“…Over the past few years, many academics have put forward control strategies, such as robust control [15,16], feedforward control [17], adaptive control [18][19][20], fuzzy control [21][22][23], optimal control [24,25], and so on. However, the control strategy proposed in aforementioned papers can only deal with systems whose dynamics are linearly characteristic.…”

Research on water and fertilizer PH control strategy of automatic fertilizer application system in cotton field

“…designed a controller based on BP neural network PID with PSO optimization for the accurate control of liquid fertilizer flow, which was experimentally verified to have good control accuracy and robustness. Shan Y C et al [10]. designed an improved fuzzy PID Smith prediction compensation algorithm based on the improved fuzzy PID Smith prediction compensation algorithm to solve the control quality degradation caused by the mismatch between the model and the control system, and the algorithm was experimentally verified to be practical and robust.…”