In order to mitigate time-varying, lag, and nonlinearity impacts on fertilization systems and achieve precise control of liquid conductivity, we propose a novel hybrid-optimized fractional-order proportional-integral-derivative (PID) algorithm. This algorithm utilizes a fuzzy algorithm to tune the five parameters of the fractional-order PID algorithm, employs the Smith predictor for structural optimization, and utilizes Wild Horse Optimizer, improved by genetic algorithms, to optimize fuzzy rules. We conducted MATLAB simulations, precision experiments, and stability tests on this controller. MATLAB simulation results, along with precision experiment results, indicate that compared to PID controllers, Smith predictor-optimized PID controllers, and fuzzy-tuned fractional-order PID controllers, the proposed controller has the narrowest steady-state conductivity range, the shortest settling time, and the lowest overshoot, showcasing excellent overall dynamic performance. Stability test results demonstrate that the controller maintains stable operation under different pressure conditions. Therefore, this control system from our study achieves superior control effectiveness, providing a viable approach for the control of nonlinear time-delay systems.

Xinjiang is the largest cotton-producing region in China, but it faces a severe shortage of water resources. According to relevant studies, the cotton yield does not significantly decrease under appropriate limited water conditions. Therefore, this paper proposes a water and fertilizer integrated control system to achieve water and fertilizer conservation in the process of cotton field cultivation. This paper designs a fuzzy neural network Proportional–Integral–Derivative controller based on the improved Wild Horse Optimizer to address the water and fertilizer integrated control system’s time-varying, lag, and non-linear characteristics. The controller precisely controls fertilizer electrical conductivity (EC) by optimizing parameters through an improved Wild Horse Optimizer for the initial weights from the normalization layer to the output layer, the initial center values of membership functions, and the initial base width of membership functions in the fuzzy neural network. The performance of the controller is validated through MATLAB simulation and experimental tests. The results indicate that, compared with conventional PID controllers and fuzzy PID controllers, this controller exhibits excellent control accuracy and robustness, effectively achieving precise fertilization.

Xinjiang, China's largest cotton-producing region, suffers from a severe shortage of water resources. Cotton is a highly adapted crop to limited water conditions and no significant decreases in cotton yield have been observed under appropriate limited water conditions. Therefore, water and fertilizer integration technology has become the fundamental way out for the development of the cotton industry in Xinjiang. Time-varying, hysteresis and non-linearity in automatic fertilizer application systems for cotton fields. To address this problem, a fuzzy PID controller based on genetic algorithm optimisation is studied and designed in this paper. Optimisation of the control parameters of the PID is achieved through the optimisation of the proportionality factor in the fuzzy control algorithm by means of a genetic algorithm to complete the precise control of the fertilizer pH value. Development of an automatic fertilizer control system based on a real-time fuzzy control system using MATLAB and PLC controllers with OPC technology. The performance of the controller was verified experimentally and the results showed that the controller has good control accuracy and robustness compared with conventional PID controllers and fuzzy PID controllers, achieving the effect of precise fertilizer application.